
# Write print and then () and inside bracket add "" and write any thing you want to print 
print("Hello World")
print(2+3)
print("Python with Ammar")

Hello World
5
Python with Ammar


# Addition
print(2+3)
# Subtraction
print(3-2)
# Multiplication
print(48*3)
#Divivion for getting a floating no. like 3.25
print(22/3) #floating numbers
# Division for getting a whole no 
print(11//3) #for whole number 
# Getting a power
print(2**3)
# Getting a percentage 
print(34%2)
# Adding simultaniouly all funtion according to PEMDAS
print(2**3/2*3/3+6-4+2)

#PEMDAS
#Parenthesis Exponenets Multiply Addition Substraction
#left to right sequencefor M D & A S*

5
1
144
7.333333333333333
3
8
0
8.0


# Anything we write inside the "" is our string 
print("Hello World")
print("Python with Ammar")
print('test for single quote')
print("test for double quote")
print('''test for trriple quotes''')
print("Whats's Up")
print("  what's up    ?")
print("srtring_clear")

Hello World
Python with Ammar
test for single quote
test for double quote
test for trriple quotes
Whats's Up
  what's up    ?
srtring_clear


# Adding anything to comment by pressing (Ctrl+/) 
print("How are you?") #press these to comment out (Ctrl+/)
print (" We are learning python with ammar") #print a string
print(2+3) #print operators function withnumbers

How are you?
 We are learning python with ammar
5


#Variables: objects containing specific values 
x=5 #Numeric or integer variable
print (x)
y= "we are learning Python with ammar" #string variable 
print(y)
x= x+15
print(x) #answer will be 20 beacause lines are updating from upward to downward continuously

#types/class of variables
type(x)
print(type(x))
print(type(y))

#print_type_class

#rules to assign a variable 
#1 . Variable should contains letter , numbers or underscores
#2 . Do not start with numbers Like 2y instead of y only
#3 . Spaces are not allowed in variable name 
#4 . Do not use keywords used in functions like break ,  mean , media, test, etc
#5 . Short and Descriptive 
#6 . Case sensitivity (Lowercase , upper case letters , lower case letters should be used)

fruit_basket= 10
fruit_basket= "Mangoes"
print(type (fruit_basket))
#del fruit_basket
print(fruit_basket)

5
we are learning Python with ammar
20
<class 'int'>
<class 'str'>
<class 'str'>
Mangoes


# greetings= "Assalam-u-Alikum "
# asking=",kia hal hain?"
# print(greetings,name,asking)

#3rd Stage input function
name=input("what is your name? ")
age=input("How old are you? ")
greetings="Hello"

print(greetings,name,",you are still young bro ")

#input_ammar_ You are still young

what is your name? Muzammil 
How old are you? 18
Hello Muzammil  ,you are still young bro


#logical operators are either "true or false or yes or no or 0 or 1"
#equal to                   ==
#not equal to               !=
#less than                  <
#greater than               >
#less then and equal to     <=
#greater than and wqualto   >=

# question.. is 4 equal to 4
# print(4==4)
# print(4!=4)
# print(4>3)
# print(5<4)
# print(3<=5)
# print(3>=5)

# #application of logical operators 
# hammad_age=4
# age_at_school=5
# print(hammad_age==age_at_school)

#input function and logical operator
age_at_school= 5 #variable
hammad_age=input("What is the age of Hammad ?") #input function
hammad_age=int(hammad_age) #changing type of variavble
print(type(hammad_age))
print(hammad_age==age_at_school) #logical operator

#convert input

What is the age of Hammad ?5
<class 'int'>
True


# x=10        #integer
# y=10.2      #float
# z="hello"   #string

# print(type(x))
# print(type(y))
# print(type(z))

#implicit type conversion
# x=x*y

# print(x, type(x))

#explicit type conversion
# age=input("what is your age? ")
# print(type(float(age)))
# # age=int(age)
# # print(type(int(age)))
# print(age,type(str(age)))

name=input("what is your name ?")
print(name, type(str(name)))

#type_conversion

what is your name ?Muzammil
Muzammil <class 'str'>


required_age_at_school=4
hammad_age=1

#question: Can hammad goto school 
if hammad_age==required_age_at_school:
    print("Congratulation!! Hammad can join the school")
elif hammad_age > required_age_at_school :
    print("Hammad should join higher school")
elif hammad_age<=2:
    print("You should take care of Hammad he is still a baby ")
else:
    print("Hammad Can not go to school ")

#i, elif, else statement clear .

You should take care of Hammad he is still a baby


#1

#defining a functions
# def print_codanics():
#      print("We are learning with ammar")
#      print("We are learning with ammar")
#      print("We are learning with ammar")
    
# print_codanics()

#2
# def print_code():
#     text= "we are learning python with ammar "
#     print(text)
#     print(text)
#     print(text)

# print_code()

#3
# def print_code(text):
#     print(text)
#     print(text)
#     print(text)

# print_code("We are learning python")

#4
#defining a function with if elif and else statement

# def school_calculator(age):
#     if age==5:
#         print("Hammad can join the school")
#     elif age>5:
#         print("Hammad should go to higher school")
#     else:
#         print("Hammad is still a baby")

# school_calculator(5)

#defining a function of future 
# def future_age(age):
#     new_age= age+20
#     return new_age
#     print(new_age)
#     # print(new_age)
# furture_age=future_age(3)
# print(furture_age)

#i understand functions really well

# def repeat_ali_4times():
#     text= ("ALI")
#     print(text)
#     print(text)
#     print(text)
#     print(text)

# repeat_ali_4times()


#practice again 
# text= input("what do you want to write 5 times")
# def write_4_times(text):
#     print(text)
#     print(text)
#     print(text)
#     print(text)

# write_4_times(text)
name=input("What is your name? ")
age=int(input("What is your age ? "))
greetings= ("Hello")

def school_extrance_calculator(age):
    print(greetings,name)
    if age>=5 and age<9:
        print("You are welcome to school")
    elif age<5:
        print("You are not eligible")
    elif age>=10 and age<15:
        print("You should go to higher school")
    else:
        print("you should go to university")

school_extrance_calculator(age)

What is your name? Muzammil
What is your age ? 8
Hello Muzammil
You are welcome to school


# #while loops and for loops 
# # while loops 

# x=0
# while(x<=5):
#     print (x)
#     x=x+1


#for loop
# for x in range (4,11):
#     print(x)


#array 
days= ["mon", "tue", "wed", "thurs", "fri", "sat", "sund"]
for d in days:
    if d=="fri": break #Stop the loop
    # if d=="fri": continue #skip that entity 
    print(d)

mon
tue
wed
thurs


#if you want to print the value of pi
import math
print("The value of pi is ",math.pi)
print(type(math.pi))

The value of pi is  3.141592653589793
<class 'float'>


import statistics
x= [150, 250,350,450]
print(statistics.mean(x))
#some important libraries
#numpy, pandas

The value of pi is  3.141592653589793
<class 'float'>
300


#print(We are learning) #syntax error
#print(25/0) #runtime error

name="ammar"
print("Hello", name)

#trouble shooting is easy

Hello ammar


name= input("What is your name ? ")
print(name)
age= int(input("What is your age? "))
print(age)
print(type(age))

if age==24:
    print(name,"You are still young bro")
elif age<24:
    print(name, "Bachay you are still a baby")
elif age>24 and age<100:
    print(name ,"saab Babay ho rhe ho , shadi krwa lo")
elif age>=100 and age<200:
    print(name, "Mar ja bhai ") 
else:
    print("Tu mar chuka hai ")

What is your name ? Muzammil
Muzammil
What is your age? 24
24
<class 'int'>
Muzammil You are still young bro


#units of BMI is wiegh in kg / height in m and its square 
name= input("What is your name ?  ")
greetings= ("Hello" ,name)
greetings
weight= float(input("what is your weight?  "))
height= float(input("and your height ?  "))


bmi= weight/height**2
bmi

print(name ,"your BMI IS", bmi)

What is your name ?  Muzammil 
what is your weight?  100
and your height ?  150
Muzammil  your BMI IS 0.0044444444444444444


#make a string
a= "Samosa Pakora"
a

'Samosa Pakora'


#checking the value at index 0
a[0]
#counting will star from 0 to onwards in python

'S'


a[1]

'a'


a[2]

'm'


a[6]
#it will print a space

' '


len(a)
#it will show the number of index in our string

13


a[0:6]
#the last no will be excludes like i ask for letters from 0 to 6 but it will print from 0 to 5 letters

'Samosa'


a[1:8]
#here P is 7th character as we strat counting from 0

'amosa P'


a[0:13] #here if we count from 0 to 13 it will be total 14 characters here 13 no is exclusive and count complterd from 0 to 12 total as 13

'Samosa Pakora'


a[-2]
#here it starts from right side and will start from number (-1)

'r'


a[-1:-6]

#here it will not print any thing

''


a[-6:-1]
#here we see the writing sequence in string will remain same from right to left
#also -1 no is "a" but it will not print here as last no is exclude

'Pakor'


a[-6:0]

''


a[-6:13]

'Pakora'


food= "birYani"
food

'birYani'


food

'birYani'


#Checking the length 
len(food)

7


# Capitalize 
food.capitalize()

'Biryani'


#Upper case letters
food.upper()

'BIRYANI'


#lower case letters
food.lower()

'biryani'


#replace
food.replace("b", "sh")

'shirYani'


#counting a specific alphabet in a string 
name = "baba_aammar with Dr aamar tufail"
name

'baba_aammar with Dr aamar tufail'


name.count("a")

9


name.count("D")

1


#how to find a number of index in string
name = "baba_aammar with Dr aamar tufail"
name

'baba_aammar with Dr aamar tufail'


name.find("t")

14


# how to split a string
food = "i love samosa , pakora , raita, biryani and karahi"
food

'i love samosa , pakora , raita, biryani and karahi'


food.split(",")

['i love samosa ', ' pakora ', ' raita', ' biryani and karahi']


tup1 = (1,"python" , True , 2.5)
tup1

(1, 'python', True, 2.5)


#type of a tuple 
type(tup1)

tuple


tup1[1]

'python'


tup1[0]

1


tup1[0:6]

(1, 'python', True, 2.5)


tup1[0:3] # last element is exclusive

(1, 'python', True)


#length of tuple
len(tup1)

4


tup2 = (2, "baba ammar", 3.5, False )
tup2

(2, 'baba ammar', 3.5, False)


# concatinate ( TO add two or more tuple)
tup1+tup2

(1, 'python', True, 2.5, 2, 'baba ammar', 3.5, False)


#concatinate + repeat 
tup1*3 + tup2

(1,
 'python',
 True,
 2.5,
 1,
 'python',
 True,
 2.5,
 1,
 'python',
 True,
 2.5,
 2,
 'baba ammar',
 3.5,
 False)


tup1*2 + tup2

(1, 'python', True, 2.5, 1, 'python', True, 2.5, 2, 'baba ammar', 3.5, False)


tup3 = (20, 50, 60, 80, 96)
tup3

(20, 50, 60, 80, 96)


max(tup3)

96


min(tup3)

20


tup3*2

(20, 50, 60, 80, 96, 20, 50, 60, 80, 96)


list1 = [2, "baba ammar" , False]
list1

[2, 'baba ammar', False]


type(list1)

list


len(list1)

3


list1[2]

False


list2 = [3, 5, "Aammar", "Codanics", 478, 53.2, True]
list2

[3, 5, 'Aammar', 'Codanics', 478, 53.2, True]


list1 + list2

[2, 'baba ammar', False, 3, 5, 'Aammar', 'Codanics', 478, 53.2, True]


list1*2

[2, 'baba ammar', False, 2, 'baba ammar', False]


list1

[2, 'baba ammar', False]


list1.reverse()
list1

[False, 'baba ammar', 2]


list1.append("codanics youtube channel")
list1

[False, 'baba ammar', 2, 'codanics youtube channel']


list1.count(False)

1


list3 = [20,30,40,50,60,52,562,488,2485]
list3

[20, 30, 40, 50, 60, 52, 562, 488, 2485]


len(list3)

9


#sorting a List
list3.sort()
list3

[20, 30, 40, 50, 52, 60, 488, 562, 2485]


#repeat
list3*3

[20,
 30,
 40,
 50,
 52,
 60,
 488,
 562,
 2485,
 20,
 30,
 40,
 50,
 52,
 60,
 488,
 562,
 2485,
 20,
 30,
 40,
 50,
 52,
 60,
 488,
 562,
 2485]


list2+list3

[3,
 5,
 'Aammar',
 'Codanics',
 478,
 53.2,
 True,
 20,
 30,
 40,
 50,
 52,
 60,
 488,
 562,
 2485]


lists= list1 +list2
lists

[False,
 'baba ammar',
 2,
 'codanics youtube channel',
 3,
 5,
 'Aammar',
 'Codanics',
 478,
 53.2,
 True]


#Food and thier prices 
food1= {"Samosa" : 30, "Pakora" : 100, "Raita" : 20, "Salad" : 50, "Chicken Rolls": 30,}
food1

{'Samosa': 30, 'Pakora': 100, 'Raita': 20, 'Salad': 50, 'Chicken Rolls': 30}


type(food1)

dict


#extract data 
keys= food1.keys()
keys

dict_keys(['Samosa', 'Pakora', 'Raita', 'Salad', 'Chicken Rolls'])


values = food1.values()
values

dict_values([30, 100, 20, 50, 30])


#adding new element
food1["Tikki"]=10
food1

{'Samosa': 30,
 'Pakora': 100,
 'Raita': 20,
 'Salad': 50,
 'Chicken Rolls': 30,
 'Tikki': 10}


#updating a values
food1["Tikki"]= 15
food1

{'Samosa': 30,
 'Pakora': 100,
 'Raita': 20,
 'Salad': 50,
 'Chicken Rolls': 30,
 'Tikki': 15}


food2 = {"Dates": 50, "Chocolates":200, "Sawayyan":1000}
food2

{'Dates': 50, 'Chocolates': 200, 'Sawayyan': 1000}


#Concatinate
food1.update(food2)
food1

{'Samosa': 30,
 'Pakora': 100,
 'Raita': 20,
 'Salad': 50,
 'Chicken Rolls': 30,
 'Tikki': 15,
 'Dates': 50,
 'Chocolates': 200,
 'Sawayyan': 1000}


s1= {1, 2, 2.2, 5, "Codanics", "Faisalabad", True}
s1
# here we see that boolean operator dosent print in sets

{1, 2, 2.2, 5, 'Codanics', 'Faisalabad'}


s1.add("codanics")
s1

{1, 2, 2.2, 5, 'Codanics', 'Faisalabad', 'codanics'}


s1.add("Faisalabad")
s1

{1, 2, 2.2, 5, 'Codanics', 'Faisalabad', 'codanics'}


s1.remove("codanics")
s1

{1, 2, 2.2, 5, 'Codanics', 'Faisalabad'}


import seaborn as sns
import matplotlib.pyplot as plt 
sns.set_theme(style="ticks", color_codes= True)
titanic = sns.load_dataset("titanic")
sns.catplot(x="sex", y="survived", hue="class", kind="bar", data=titanic)

<seaborn.axisgrid.FacetGrid at 0x14152a7df40>


import seaborn as sns
import matplotlib.pyplot as plt 
sns.set_theme(style="ticks", color_codes=True)


titanic= sns.load_dataset("titanic")
titanic


p1=sns.countplot(x="who", data=titanic, hue="alone")
p1.set_title("PLot for Counting")

Text(0.5, 1.0, 'PLot for Counting')


#scatter plot 
import seaborn as sns
import matplotlib.pyplot as plt


sns.set_theme(style="ticks", color_codes=True)
titanic = sns.load_dataset("titanic")
g=sns.FacetGrid(titanic, row="sex", hue= "alone")
g=(g.map(plt.scatter,"age", "fare").add_legend())


import seaborn as sns
import matplotlib.pyplot as plt 

#load data set 

phool = sns.load_dataset("iris")
phool

#draw a line plot
# sns.lineplotin(x="", y="", data=phool)
#lie plot always work betwwen numeric values


sns.lineplot(x="sepal_length", y="sepal_width", data=phool)

plt.show()


#Program with title 

import seaborn as sns
import matplotlib.pyplot as plt 

phool = sns.load_dataset("iris")
sns.lineplot(x="sepal_length", y="sepal_width", data=phool)
plt.title("Phoolo ka Plot")
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt 

phool = sns.load_dataset("iris")
sns.lineplot(x="sepal_length", y="sepal_width", data=phool)
plt.title("Phoolo ka Plot")
plt.xlim(2)
plt.ylim(1)
plt.show()


#to remove alreay or default style first 
sns.set_style(style= None , rc=None )


import seaborn as sns
import matplotlib.pyplot as plt 

phool = sns.load_dataset("iris")
sns.lineplot(x="sepal_length", y="sepal_width", data=phool)
plt.title("Phoolo ka Plot")
#sns.set_style(style= None , rc=None)
sns.set_style("darkgrid")
plt.xlim(2)
plt.ylim(1)
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt 

phool = sns.load_dataset("iris")
#after loading data set 
#changing size of final figure 
plt.figure(figsize=(12,10))
sns.lineplot(x="sepal_length", y="sepal_width", data=phool)
plt.title("Phoolo ka Plot")
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt 
phool = sns.load_dataset("iris")
plt.figure(figsize=(12,10))
sns.lineplot(x="sepal_length", y="sepal_width", data=phool)
plt.title("Phoolo ka Plot")
sns.set_style(style= None , rc=None)
sns.set_style("darkgrid")
plt.xlim(3)
plt.ylim(1.5)
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt 

phool = sns.load_dataset("iris")
phool 

sns.barplot(x="species", y="sepal_width", data=phool)
plt.title("Phoolo ka Plot")
plt.show()


phool


import seaborn as sns
import matplotlib.pyplot as plt 

phool = sns.load_dataset("iris")
phool 

sns.barplot(x="species", y="petal_length", data=phool)
plt.title("Phoolo ka Plot")
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
kashti


import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="who",y="alone", data=kashti)
plt.title("titanic ka Plot")
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="sex",y="alone", hue="who", data=kashti)
plt.title("titanic ka Plot")
plt.show()


##  order setting 
import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="sex",y="alone", hue="who", data=kashti, order=["female" ,"male"] )
plt.title("titanic ka Plot")
plt.show()


#changing color 
##  order setting 
import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="sex",y="alone", hue="who", data=kashti, order=["female" ,"male"] ,color= "grey")
plt.title("titanic ka Plot")
plt.show()


#removing error bars
import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="sex",y="alone", hue="who", data=kashti, order=["female" ,"male"] ,ci= None)
plt.title("titanic ka Plot")
plt.show()


#using different paletts
import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="sex",y="alone", hue="who", data=kashti, order=["female" ,"male"] ,ci= None , palette= "pastel")
plt.title("titanic ka Plot")
plt.show()

#we can search for seaborne built in color palettes


# intensity of colors 
import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="sex",y="alone", hue="who", data=kashti, order=["female" ,"male"] ,ci= None , saturation=.5)
plt.title("titanic ka Plot")
plt.show()


#HOrizontal Plot 
#numeric parameter on x axis 
import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="fare", y="class", hue="sex", data=kashti ,ci= None)
plt.title("titanic ka Plot")
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="fare", y="class", hue="sex", data=kashti ,ci= None)
plt.title("titanic ka Plot")
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt 

kashti = sns.load_dataset("titanic")
sns.barplot(x="class", y="fare", data=kashti ,linewidth=2.5,facecolor=(1,1,1,1) , errcolor= "0.5" , edgecolor= "0.5" )

plt.title("titanic ka Plot")
plt.show()


import seaborn as sns 
import matplotlib.pyplot as plt 
#canvas (baloon board)
sns.set(style="whitegrid")
kashti= sns.load_dataset("titanic")

sns.boxplot(x="class",y="fare", data=kashti)
plt.show()


import seaborn as sns 
import matplotlib.pyplot as plt 
sns.set(style="whitegrid")
tip= sns.load_dataset("tips")
tip


import seaborn as sns 
import matplotlib.pyplot as plt 
import numpy #estimator will not work here
sns.set(style="whitegrid")
tip= sns.load_dataset("tips")

sns.boxplot(x="day",y="tip", data=tip, saturation=1)
plt.show()


import seaborn as sns 
import matplotlib.pyplot as plt
import pandas as pd 
import numpy as np

sns.set(style="whitegrid")
tip= sns.load_dataset("tips")
tip.describe()


#  catagorical variable must be draw in x-axis or hue
#     numeric variable in y-axis
#     numerical variable should not be go to hue


#for single 

import seaborn as sns
sns.set(style="whitegrid")
tip= sns.load_dataset("tips")

sns.boxplot(x=tip["tip"])

<AxesSubplot:xlabel='tip'>


import seaborn as sns
sns.set(style="whitegrid")
tip= sns.load_dataset("tips")
sns.boxplot(y=tip["total_bill"])

<AxesSubplot:ylabel='total_bill'>


import seaborn as sns
sns.set(style="whitegrid")
tip= sns.load_dataset("tips")
sns.boxplot(x="tip", y="day", data= tip)

<AxesSubplot:xlabel='tip', ylabel='day'>


import seaborn as sns
sns.set(style="whitegrid")
tip= sns.load_dataset("tips")
sns.boxplot(x="tip", y="day", hue="smoker" ,data= tip, palette ="Set2")

<AxesSubplot:xlabel='tip', ylabel='day'>


#dodge
import seaborn as sns
sns.set(style="whitegrid")
tip= sns.load_dataset("tips")
sns.boxplot(x="tip", y="day", hue="smoker" ,data= tip, palette ="Set2", dodge=True)

<AxesSubplot:xlabel='tip', ylabel='day'>


import seaborn as sns
sns.set(style="whitegrid")
tip= sns.load_dataset("tips")
sns.boxplot(x="tip", y="day", hue="smoker" ,data= tip, palette ="Set2", dodge=False)

<AxesSubplot:xlabel='tip', ylabel='day'>


import seaborn as sns 
import matplotlib.pyplot as plt 
import numpy
sns.set(style="whitegrid")
tip= sns.load_dataset("tips")
my_color= {"Yes":"#0d9ea8","No":"#6e0c11"}

sns.boxplot(x="tip",y="day", hue="smoker", data=tip, saturation=1 ,palette=my_color )
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt 
import pandas as pd 
import numpy as np

kashti= sns.load_dataset("titanic")
kashti.head()


import seaborn as sns
import matplotlib.pyplot as plt 
import pandas as pd 
import numpy as np

kashti= sns.load_dataset("titanic")
sns.boxplot(x="survived", y="age", data= kashti)

<AxesSubplot:xlabel='survived', ylabel='age'>


kashti= sns.load_dataset("titanic")
p1 = sns.boxplot(x="survived", y="age", showmeans=True, data= kashti)
p1

<AxesSubplot:xlabel='survived', ylabel='age'>


kashti= sns.load_dataset("titanic")
p1 = sns.boxplot(x="survived", y="age", showmeans=True, meanprops={"marker":"+", "markersize":"12", "markeredgecolor":"red"} ,data= kashti)
p1

<AxesSubplot:xlabel='survived', ylabel='age'>


import matplotlib.pyplot as plt
import seaborn as sns 
import numpy as np
import pandas as pd

kashti= sns.load_dataset("titanic")
sns.boxplot(x="survived", y="age", showmeans=True, meanprops={"marker":"+", "markersize":"12", "markeredgecolor":"red"} ,data= kashti)
plt.title("Kitne doobay or kitne bachay" , size= 25, weight="bold")
plt.xlabel ("Kitnay bach gaey ", size= 16)
plt.ylabel ("umar kia hai", size = 16   )

Text(0, 0.5, 'umar kia hai')


#imporing libraries
import seaborn as sns 
import matplotlib.pyplot as plt 
import pandas
#setting canvas 
sns.set(style="darkgrid")
sns.set_style(style= None , rc=None)
#loading dataset 
tip= sns.load_dataset("tips")
#adjusting size of my final graph
plt.figure(figsize=(12,12))
#making our palette for diffining colors for each hue from hex color picker 
my_color= {"Yes":"#0d9ea8","No":"#6e0c11"}
#plotting a graph 
sns.boxplot(x="tip",y="day", hue="smoker", data=tip, saturation=1 ,palette=my_color )
plt.show()


import seaborn as sns 
import matplotlib.pyplot as plt 
import pandas as pd
import numpy as np
#importing data set 
my_data= pd.read_csv("new_data.csv")
my_data.head()


#importing libraries 
import seaborn as sns 
import matplotlib.pyplot as plt 
import pandas as pd
import numpy as np
#importing data set 
my_data= pd.read_csv("new_data.csv")
#setting canvas 
sns.set(style="darkgrid")
#setting final figure size 
plt.figure(figsize=(18,12))


sns.boxplot(x="Qualification", y="Weight_in_kg" ,hue="Gender", dodge= True ,data= my_data, saturation= 1, showmeans=True, meanprops={"marker":"+", "markersize":"15", "markeredgecolor":"red"})
plt.title("Kon Ziada Khata hai ?", size= 28, weight= "bold")
plt.show()


import plotly.express as px
import pandas as pd 
data= pd.read_csv("new_data.csv")
fig = px.scatter(data, x="Weight_in_kg", y="Working experience", color="Gender", symbol="Marital Status?", facet_col="Qualification",
          labels={"Gender": "Sex", "Marital Status?": "Married"})
fig.show()


import plotly.express as px
import pandas as pd 
data= pd.read_csv("new_data.csv")
fig = px.scatter(data, x="Age", y="Working experience", color="Gender", symbol="Marital Status?", facet_col="Qualification",
          labels={"Gender": "Sex", "Marital Status?": "Married"})
fig.show()


import plotly.express as px
import pandas as pd 
data= pd.read_csv("new_data.csv")
fig = px.scatter(data, x="Weight_in_kg", y="Age", color="Location",
    size="Working experience", size_max=45, log_x=True)

fig.update_layout(legend=dict(
    orientation="h",
    yanchor="bottom",
    y=1,
    xanchor="right",
    x=1
))

fig.show()


import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="darkgrid")
plt.figure(figsize=(10,10))
import pandas as pd 
my_data= pd.read_csv("new_data.csv")
sns.boxenplot(x="Location", y="Weight_in_kg",
              color="b",
              scale="linear", data=my_data)

plt.title("Konsi Country k loog ziada khate hain" ,size=25 , weight="bold")

Text(0.5, 1.0, 'Konsi Country k loog ziada khate hain')


import plotly.express as px
import pandas as pd 
data= pd.read_csv("new_data.csv")
fig = px.area(data, x="Age", y="Weight_in_kg", color="Qualification", line_group="Location")
fig.show()


import plotly.express as px
import pandas as pd 
data= pd.read_csv("new_data.csv")

fig = px.sunburst(data, path=['Qualification', 'Location'], values='Age',
                  color='Working experience', hover_data=['Marital Status?'])
fig.show()


import plotly.express as px
import pandas as pd 
data= pd.read_csv("new_data.csv")
fig = px.violin(data, y="Age", x="Marital Status?", color="Gender", box=True, points="all", hover_data=data.columns)
fig.show()


import plotly.express as px
import pandas as pd 
data= pd.read_csv("new_data.csv")

fig = px.density_contour(data, x="Age", y="Weight_in_kg")
fig.show()


import plotly.express as px
import pandas as pd 
data= pd.read_csv("new_data.csv")

fig = px.line_polar(data, r="Age", theta="Location", color="Qualification", line_close=True,
            color_discrete_sequence=px.colors.sequential.Plasma_r)
fig.show()


import plotly.express as px
import pandas as pd 
data= pd.read_csv("new_data.csv")
fig = px.scatter(data, x="Weight_in_kg", y="Height_in_cm", color="Qualification", marginal_y="violin",
           marginal_x="box", trendline="ols", template="simple_white", labels={"Height_in_cm" : "Height", "Weight_in_kg": "Weight", "Male": "Munda" })

fig.show()


import numpy as np


a= np.array([1,2,3,4,5])
a

array([1, 2, 3, 4, 5])


type(a)

numpy.ndarray


len(a)

5


# creating a single axis array of number zero 
c= np.zeros(2)
c

array([0., 0.])


# creating a single axis array of number One 
d= np.ones(10)
d

array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])


e=np.empty(3)
e

array([9.96959917e-312, 0.00000000e+000, 4.94065646e-324])


# with the specific range of elements 
g= np.arange(5,15) # as we arleady know the last no. is exclusive 
g

array([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14])


# with the range of elements with a speciefied gap
h=np.arange(2,20,2) # goin from 2 to 20 with a specified gap of 2 and last no. is exlcusive
h

array([ 2,  4,  6,  8, 10, 12, 14, 16, 18])


# and if we want 20 no. also in last arange
h=np.arange(2,21,2)
h

array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20])


# Linearly spaced arrays 
i= np.linspace(0,15 , num= 5) # GOing 0 to 15 in just 5 numbers in a way that the distance between each no. will remain same
i

array([ 0.  ,  3.75,  7.5 , 11.25, 15.  ])


j= np.ones(5, dtype=np.float64)
j

array([1., 1., 1., 1., 1.])


b= np.array([[2,2,2,2],[3,3,3,3]])
b

array([[2, 2, 2, 2],
       [3, 3, 3, 3]])


e= np.array([[1,1,1,1],[2,2,2,2]])
e

array([[1, 1, 1, 1],
       [2, 2, 2, 2]])


k=np.zeros((3,4))
k

array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]])


l=np.zeros((5,6))
l

array([[0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.]])


m=np.ones((2,4))
m

array([[1., 1., 1., 1.],
       [1., 1., 1., 1.]])


d= np.array([[2,2,2],[2,2,2],[6,5,4]])
d

array([[2, 2, 2],
       [2, 2, 2],
       [6, 5, 4]])


f= np.array ([[4,5,6,4],[8,6,4,5],[8,6,4,2],[9,6,3,2]])
f

array([[4, 5, 6, 4],
       [8, 6, 4, 5],
       [8, 6, 4, 2],
       [9, 6, 3, 2]])


g= np.array([[1,2,3],[2,3,4],[4,5,6]])
g

array([[1, 2, 3],
       [2, 3, 4],
       [4, 5, 6]])


# TensorFlow is a library use for 3 Dimensional things
# TensorFlow is also a free and open source software libraryfor machine learning and artificial intelligence


#making and reshaping a 3D array
c= np.arange(24) .reshape (2,3,4) # First axis has  length = 2,,Second Axis has length = 3 , Third axis has length = 4
c

array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],

       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]])


d= np.zeros((2,3,3))
d

array([[[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]]])


f= np.ones((3,4,5) , dtype= np.int64)
f

array([[[1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1]],

       [[1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1]],

       [[1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1]]], dtype=int64)


f= np.array ([[[4,5,6,4],[8,6,4,5],[8,6,4,2],[9,6,3,2]],[[4,5,6,4],[8,6,4,5],[8,6,4,2],[9,6,3,2]],[[4,5,6,4],[8,6,4,5],[8,6,4,2],[9,6,3,2]]])
f

array([[[4, 5, 6, 4],
        [8, 6, 4, 5],
        [8, 6, 4, 2],
        [9, 6, 3, 2]],

       [[4, 5, 6, 4],
        [8, 6, 4, 5],
        [8, 6, 4, 2],
        [9, 6, 3, 2]],

       [[4, 5, 6, 4],
        [8, 6, 4, 5],
        [8, 6, 4, 2],
        [9, 6, 3, 2]]])


z= np.array ([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]],[[13,14,15],[16,17,18]]])
z

array([[[ 1,  2,  3],
        [ 4,  5,  6]],

       [[ 7,  8,  9],
        [10, 11, 12]],

       [[13, 14, 15],
        [16, 17, 18]]])


#importing numpy library 
import numpy as np


import numpy as np
food= np.array(["pakora" , "samosa" , "raita"])
food

array(['pakora', 'samosa', 'raita'], dtype='<U6')


price = np.array([5,5,5])
price

array([5, 5, 5])


#Checking type of array 
type(price)

numpy.ndarray


type(food)

numpy.ndarray


#length of array 
len(food)

3


#indexing 
price[2]

5


price[0:]

array([5, 5, 5])


#index no to find the index in an array 
food[1]

'samosa'


price.mean()

5.0


# zeros method 
a= np.zeros(6)
a

array([0., 0., 0., 0., 0., 0.])


# ones method 
b= np.ones(5)
b

array([1., 1., 1., 1., 1.])


c= np.empty(5)
c

array([1., 1., 1., 1., 1.])


# Making a Range 
a= np.arange(10)
a

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])


# Specified range 
a= np.arange(2,21)
a

array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
       19, 20])


# specific arang with specific distance 
a= np.arange (2,20,3)
a

array([ 2,  5,  8, 11, 14, 17])


#table of 5
a= np.arange (5,55,5)
a

array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50])


# with line space 
a= np.linspace (0,10, num = 6 ,dtype= np.int64 )
a

array([ 0,  2,  4,  6,  8, 10], dtype=int64)


b= np.linspace(1,100, num = 40)
b

array([  1.        ,   3.53846154,   6.07692308,   8.61538462,
        11.15384615,  13.69230769,  16.23076923,  18.76923077,
        21.30769231,  23.84615385,  26.38461538,  28.92307692,
        31.46153846,  34.        ,  36.53846154,  39.07692308,
        41.61538462,  44.15384615,  46.69230769,  49.23076923,
        51.76923077,  54.30769231,  56.84615385,  59.38461538,
        61.92307692,  64.46153846,  67.        ,  69.53846154,
        72.07692308,  74.61538462,  77.15384615,  79.69230769,
        82.23076923,  84.76923077,  87.30769231,  89.84615385,
        92.38461538,  94.92307692,  97.46153846, 100.        ])


# specifing the data type 
a= np.ones(5, dtype= np.int8)
a

array([1, 1, 1, 1, 1], dtype=int8)


a= np.ones(50, dtype= np.float64)
a

array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])


a= np.array([ 10,12,15,2,4,6,18,100,18,16,10.3,0.5])
a

array([ 10. ,  12. ,  15. ,   2. ,   4. ,   6. ,  18. , 100. ,  18. ,
        16. ,  10.3,   0.5])


#sorting an array
a.sort()
a

array([  0.5,   2. ,   4. ,   6. ,  10. ,  10.3,  12. ,  15. ,  16. ,
        18. ,  18. , 100. ])


b= np.array([10.5,5,15.6,8,0.5,10.5,100.9,15,16,59])
b

array([ 10.5,   5. ,  15.6,   8. ,   0.5,  10.5, 100.9,  15. ,  16. ,
        59. ])


c= np.concatenate((a,b))
c

array([  0.5,   2. ,   4. ,   6. ,  10. ,  10.3,  12. ,  15. ,  16. ,
        18. ,  18. , 100. ,  10.5,   5. ,  15.6,   8. ,   0.5,  10.5,
       100.9,  15. ,  16. ,  59. ])


c.sort()
c

array([  0.5,   0.5,   2. ,   4. ,   5. ,   6. ,   8. ,  10. ,  10.3,
        10.5,  10.5,  12. ,  15. ,  15. ,  15.6,  16. ,  16. ,  18. ,
        18. ,  59. , 100. , 100.9])


a = np.array ([[1,2,3],[2,6,5]])
a

array([[1, 2, 3],
       [2, 6, 5]])


b = np.array ([[3,6,5],[6,8,9]])
b

array([[3, 6, 5],
       [6, 8, 9]])


#checking the shape of matrix 
b.shape

(2, 3)


c= np.concatenate((a,b) ,axis= 1)
c

array([[1, 2, 3, 3, 6, 5],
       [2, 6, 5, 6, 8, 9]])


c= np.concatenate((a,b) ,axis= 0)
c

array([[1, 2, 3],
       [2, 6, 5],
       [3, 6, 5],
       [6, 8, 9]])


c.shape

(4, 3)


a= np.array ([[['a','b','c'],['e','d','f']],
            [['a','b','c'],['e','d','f']],
            [['a','b','c'],['e','d','f']]])
a

array([[['a', 'b', 'c'],
        ['e', 'd', 'f']],

       [['a', 'b', 'c'],
        ['e', 'd', 'f']],

       [['a', 'b', 'c'],
        ['e', 'd', 'f']]], dtype='<U1')


#finding a no. of dimensions 
a.ndim

3


a.size

18


# shape of array 
a.shape

(3, 2, 3)


b= np.array ([[[1,2,3],[7,8,9],[9,6,3]],
            [[1,2,3],[7,8,9],[9,6,3]],
            [[1,2,3],[7,8,9],[9,6,3]]])
b

array([[[1, 2, 3],
        [7, 8, 9],
        [9, 6, 3]],

       [[1, 2, 3],
        [7, 8, 9],
        [9, 6, 3]],

       [[1, 2, 3],
        [7, 8, 9],
        [9, 6, 3]]])


b.ndim

3


type(a)

numpy.ndarray


b.shape

(3, 3, 3)


b.size

27


a= np.arange(9)
a

array([0, 1, 2, 3, 4, 5, 6, 7, 8])


a.reshape (3,3) #3*3=9 (9 indexes are there in array )

array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])


a.shape

(9,)


#row wise conversion
b= a[np.newaxis,:]
b

array([[0, 1, 2, 3, 4, 5, 6, 7, 8]])


b.shape

(1, 9)


#coloumn wise conversion
b= a[:, np.newaxis]
b

array([[0],
       [1],
       [2],
       [3],
       [4],
       [5],
       [6],
       [7],
       [8]])


b.shape

(9, 1)


c= np.arange(9)
c

array([0, 1, 2, 3, 4, 5, 6, 7, 8])


c.shape

(9,)


d=c[np.newaxis, :]
d

array([[0, 1, 2, 3, 4, 5, 6, 7, 8]])


d.shape

(1, 9)

a

array([0, 1, 2, 3, 4, 5, 6, 7, 8])


a[2]

2


a[0:5]

array([0, 1, 2, 3, 4])

a*6

array([ 0,  6, 12, 18, 24, 30, 36, 42, 48])

a+6

array([ 6,  7,  8,  9, 10, 11, 12, 13, 14])


a.sum()

36


a.mean()

4.0


a.max()

8


a.min()

0


#importing libraries 
import pandas as pd 
import numpy as np


# object creation 
s= pd.Series([1,2,np.nan ,5,7,8,9])
s

0    1.0
1    2.0
2    NaN
3    5.0
4    7.0
5    8.0
6    9.0
dtype: float64


dates = pd.date_range("20220101", periods=9)
dates

DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',
               '2022-01-05', '2022-01-06', '2022-01-07', '2022-01-08',
               '2022-01-09'],
              dtype='datetime64[ns]', freq='D')


dates = pd.date_range("20220101", periods=33)
dates

DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',
               '2022-01-05', '2022-01-06', '2022-01-07', '2022-01-08',
               '2022-01-09', '2022-01-10', '2022-01-11', '2022-01-12',
               '2022-01-13', '2022-01-14', '2022-01-15', '2022-01-16',
               '2022-01-17', '2022-01-18', '2022-01-19', '2022-01-20',
               '2022-01-21', '2022-01-22', '2022-01-23', '2022-01-24',
               '2022-01-25', '2022-01-26', '2022-01-27', '2022-01-28',
               '2022-01-29', '2022-01-30', '2022-01-31', '2022-02-01',
               '2022-02-02'],
              dtype='datetime64[ns]', freq='D')


df= pd.DataFrame(np.random.randn(33,5), index= dates, columns= list("ABCDE"))
df


df2= pd.DataFrame(
    {
    "A" : 1.0,
    "B": pd.Timestamp("20130102"),
    "C": pd.Series(1, index= list(range(4)), dtype="float32"),
    "D": np.array([3]*4, dtype= "int32"),
    "E": pd.Categorical(["test","train","test","train"]),
    "F": "foo"
    }
    )
df2


df2.dtypes

A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object


df2.head(2)


df.tail(2)


df2.index

Int64Index([0, 1, 2, 3], dtype='int64')


dates1 = pd.date_range("20220101", periods=20)
dates1

DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',
               '2022-01-05', '2022-01-06', '2022-01-07', '2022-01-08',
               '2022-01-09', '2022-01-10', '2022-01-11', '2022-01-12',
               '2022-01-13', '2022-01-14', '2022-01-15', '2022-01-16',
               '2022-01-17', '2022-01-18', '2022-01-19', '2022-01-20'],
              dtype='datetime64[ns]', freq='D')


df1= pd.DataFrame(np.random.randn(20,5), index= dates1, columns= list("ABCDE"))
df1


a=df1.to_numpy()

array([[ 0.38813495, -0.69450026,  1.71074173, -0.95385292,  1.64714073],
       [-0.51757465, -0.18814687,  0.63003587, -0.88199128, -0.98127153],
       [-1.67498296, -0.58663723, -1.34094674,  0.70266092, -0.92954028],
       [ 0.58190588, -0.08002672,  1.13707543,  0.77544498, -1.79604041],
       [-1.27382455,  0.31291439, -1.16531406, -0.1696396 , -0.36894076],
       [ 0.51560227,  0.49006838, -0.54523928, -0.40895489,  1.33362693],
       [ 0.16966288, -1.01929506, -0.66836035, -2.78698617, -0.19935927],
       [-0.13049661,  0.1027985 ,  0.28820314, -0.29138204, -0.36455922],
       [-0.36901525,  1.28390443,  0.83990284, -0.27945273, -1.27201643],
       [-0.81408326, -0.70523725, -1.62739717, -2.38558997, -0.14479825],
       [-1.54274494, -0.53642495,  0.48493728, -0.48697742,  0.2377637 ],
       [-0.88289022,  0.17579184, -0.13163351,  0.69920486,  0.79290814],
       [ 1.23507203,  0.64050031, -1.28316767, -1.42204502, -0.40250438],
       [-0.08571273,  0.1174964 ,  2.04783829,  0.2404063 ,  0.7843707 ],
       [ 1.15749355, -0.12804461, -0.27878909,  0.29654452,  0.37171123],
       [-0.88458023, -1.62324761,  0.4925782 ,  0.08883562, -0.04922586],
       [-1.32427812, -0.76547659,  0.90790288, -0.80856471,  0.00494294],
       [ 1.68137876, -0.30296923, -1.15878239,  1.26773   , -2.33648888],
       [ 0.82182693,  1.30762029, -0.50211862, -1.8962927 , -0.19462804],
       [-0.87570223,  0.43311031,  0.88359855,  0.56113551, -0.46648558]])


a.shape

(9,)


df2.to_numpy()

array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],
       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'],
       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],
       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']],
      dtype=object)


#details of data 
df1.describe()


#to transpose the data 
df2.T


# Sorting
df1.sort_index(axis=0, ascending=False)


df.sort_values(by="B")


df.sort_values(by="B",ascending=False)


df1["A"]

2022-01-01    0.388135
2022-01-02   -0.517575
2022-01-03   -1.674983
2022-01-04    0.581906
2022-01-05   -1.273825
2022-01-06    0.515602
2022-01-07    0.169663
2022-01-08   -0.130497
2022-01-09   -0.369015
2022-01-10   -0.814083
2022-01-11   -1.542745
2022-01-12   -0.882890
2022-01-13    1.235072
2022-01-14   -0.085713
2022-01-15    1.157494
2022-01-16   -0.884580
2022-01-17   -1.324278
2022-01-18    1.681379
2022-01-19    0.821827
2022-01-20   -0.875702
Freq: D, Name: A, dtype: float64


#filteration data with coloum wise or indexwise
df1["B"]

2022-01-01   -0.694500
2022-01-02   -0.188147
2022-01-03   -0.586637
2022-01-04   -0.080027
2022-01-05    0.312914
2022-01-06    0.490068
2022-01-07   -1.019295
2022-01-08    0.102798
2022-01-09    1.283904
2022-01-10   -0.705237
2022-01-11   -0.536425
2022-01-12    0.175792
2022-01-13    0.640500
2022-01-14    0.117496
2022-01-15   -0.128045
2022-01-16   -1.623248
2022-01-17   -0.765477
2022-01-18   -0.302969
2022-01-19    1.307620
2022-01-20    0.433110
Freq: D, Name: B, dtype: float64


# TO select data row wise 
df[0:1]


df[0:2]


df[0:10]


df[1:10]


df1.head()


# showing the only 16th row  and its values 
df.loc[dates[15]]

A   -0.152087
B    0.789873
C   -1.096439
D   -0.172554
E   -1.109436
Name: 2022-01-16 00:00:00, dtype: float64


#multiple axis lables 
df.loc[:, ["A","B"]]


df.loc["20220109":"20220113",["A","B", "C"]]


df.loc["20220109",["A","B", "C"]]

A    0.125091
B   -0.340940
C    1.023370
Name: 2022-01-09 00:00:00, dtype: float64


#Scalar value
df.at[dates[0],"A"]

-1.4246598703006963


df.iloc[3]

A    0.552770
B   -0.454804
C   -0.747360
D    1.800993
E   -0.275458
Name: 2022-01-04 00:00:00, dtype: float64


df.iloc[3:10]


#       rows col
df.iloc[0:5, 0:2]


df.iloc[:, 0:2]


df[df["A"]> 0]


df[df["A"]> 0]


# assignment :Getting non zero values in more then one column 
df[ df.iloc[:, 0:5]> 0]


df[df["A"]> 0]


df[df>0]


df[ df.iloc[0:3]> 0 ]


# IS IN Method 
df3 = df.copy()
df3


#Adding a column
df3["BABA"]= [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3]
df3


#Adding a new column having same value of previous column 
df3["Mean"] =df3["A"]
df3.head()


#Adding a column having mean of previous values
# #Assignment no 2  
df3["Mean"] =df3.mean(axis= 1)
df3.head()


df["New"]="new hai "
df.head()


df.insert(2,"Beech me ", "18+")
df.head()


df["Concatinated"]= df["A"]+df["B"]
df.head()


#importing libraries 
import pandas as pd 
import numpy as np
import seaborn as sns 
import matplotlib.pyplot as plt


kashti= sns.load_dataset("titanic")


kashti.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   survived     891 non-null    int64   
 1   pclass       891 non-null    int64   
 2   sex          891 non-null    object  
 3   age          714 non-null    float64 
 4   sibsp        891 non-null    int64   
 5   parch        891 non-null    int64   
 6   fare         891 non-null    float64 
 7   embarked     889 non-null    object  
 8   class        891 non-null    category
 9   who          891 non-null    object  
 10  adult_male   891 non-null    bool    
 11  deck         203 non-null    category
 12  embark_town  889 non-null    object  
 13  alive        891 non-null    object  
 14  alone        891 non-null    bool    
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 80.7+ KB


ks= kashti


#just to see the datset 
ks.head()


ks.shape
#Rows x column

(891, 15)


ks.tail()


ks.describe()


#  unique values 
ks.nunique()

survived         2
pclass           3
sex              2
age             88
sibsp            7
parch            7
fare           248
embarked         3
class            3
who              3
adult_male       2
deck             7
embark_town      3
alive            2
alone            2
dtype: int64


# coloumn names
ks.columns

Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',
       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',
       'alive', 'alone'],
      dtype='object')


ks["sex"].unique()

array(['male', 'female'], dtype=object)


ks['who'].unique()

array(['man', 'woman', 'child'], dtype=object)


np.union1d(ks["who"].unique(), ks["sex"].unique())

array(['child', 'female', 'male', 'man', 'woman'], dtype=object)


ks[['who', "sex"]].nunique()

who    3
sex    2
dtype: int64


# find missing valus inside 
ks.isnull()


ks.isnull().sum()

survived         0
pclass           0
sex              0
age            177
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
deck           688
embark_town      2
alive            0
alone            0
dtype: int64


# removing missing value column 
ks_clean= ks.drop (["deck"], axis= 1)
ks_clean.head()


ks_clean.isnull().sum()

survived         0
pclass           0
sex              0
age            177
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
embark_town      2
alive            0
alone            0
dtype: int64


ks_clean.shape

(891, 14)


891-177-2
#117 row in age and 2 from embarked and embark town

712


ks_clean= ks_clean.dropna()
ks_clean.head()


ks_clean.dropna().shape

(712, 14)


ks_clean.isnull().sum()

survived       0
pclass         0
sex            0
age            0
sibsp          0
parch          0
fare           0
embarked       0
class          0
who            0
adult_male     0
embark_town    0
alive          0
alone          0
dtype: int64


ks_clean.shape

(712, 14)


ks.shape

(891, 15)


ks_clean["age"].value_counts()

24.00    30
22.00    27
18.00    26
19.00    25
28.00    25
         ..
36.50     1
55.50     1
0.92      1
23.50     1
74.00     1
Name: age, Length: 88, dtype: int64


ks_clean["sex"].value_counts()

male      453
female    259
Name: sex, dtype: int64


ks_clean["sex"].value_counts()

male      453
female    259
Name: sex, dtype: int64


ks.describe()


ks_clean.describe()


ks_clean.columns

Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',
       'embarked', 'class', 'who', 'adult_male', 'embark_town', 'alive',
       'alone'],
      dtype='object')


sns.boxplot(x= "sex", y='age', data =ks_clean )
# here we can see the outlyers in age

<AxesSubplot:xlabel='sex', ylabel='age'>


sns.boxplot(y='age', data =ks_clean )

<AxesSubplot:ylabel='age'>


sns.distplot(ks_clean["age"] )

# here we are seeing the bell curve / histogram for normality check 
# here we can see that it is not perfectly bell curve means data is not perfect

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='age', ylabel='Density'>


ks_clean["age"].mean()

29.64209269662921


ks_clean.head()


# Removing an out liers 
ks_clean= ks_clean[ks_clean["age"]< 68]
ks_clean.head()


ks_clean.shape

(705, 14)


ks_clean["age"].mean()

29.21797163120567


sns.distplot(ks_clean["age"] )

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='age', ylabel='Density'>


sns.boxplot(y='age', data =ks_clean )

<AxesSubplot:ylabel='age'>


ks_clean.head()


ks_clean.boxplot()

<AxesSubplot:>


ks_clean= ks_clean[ks_clean["fare"]< 300]


ks_clean.boxplot()

<AxesSubplot:>


sns.distplot(ks_clean["fare"] )

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='fare', ylabel='Density'>


ks_clean.hist()

array([[<AxesSubplot:title={'center':'survived'}>,
        <AxesSubplot:title={'center':'pclass'}>],
       [<AxesSubplot:title={'center':'age'}>,
        <AxesSubplot:title={'center':'sibsp'}>],
       [<AxesSubplot:title={'center':'parch'}>,
        <AxesSubplot:title={'center':'fare'}>]], dtype=object)


pd.value_counts(ks_clean["survived"])

0    418
1    284
Name: survived, dtype: int64


pd.value_counts(ks_clean["survived"]).plot.bar()

<AxesSubplot:>


pd.value_counts(ks_clean["sex"]).plot.bar()

<AxesSubplot:>


ks_clean.groupby(["sex","class","who"]).mean()


cor_ks_clean= ks_clean.corr()
cor_ks_clean


sns.heatmap(cor_ks_clean)

<AxesSubplot:>


sns.heatmap(cor_ks_clean , annot=True)

<AxesSubplot:>


sns.relplot(x= "age", y="fare", hue="sex", data=ks_clean)

<seaborn.axisgrid.FacetGrid at 0x25b2f22ab90>


sns.catplot(x= "sex", y="fare",data=ks_clean)

<seaborn.axisgrid.FacetGrid at 0x25b2f39ed10>


sns.catplot(x= "sex", y="fare",data=ks_clean, kind= "bar")

<seaborn.axisgrid.FacetGrid at 0x25b2c0b28c0>


sns.catplot(x= "sex", y="age", hue= "who" , data=ks_clean, kind= "box")

<seaborn.axisgrid.FacetGrid at 0x25b2c69dc00>


sns.catplot(x= "sex", y="fare", hue= "who" , data=ks_clean, kind= "box")

<seaborn.axisgrid.FacetGrid at 0x25b3066b850>


ks_clean.head()


# log transformation 
ks_clean["fare_log"] = np.log(ks_clean["fare"])
ks_clean.head()

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\arraylike.py:364: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


sns.catplot(x= "sex", y="fare_log" , data=ks_clean, kind= "box")

<seaborn.axisgrid.FacetGrid at 0x25b306c1510>


# import libraries 
import pandas as pd 
import numpy as np 
import seaborn as sns 
import matplotlib.pyplot as plt


kashti = sns.load_dataset("titanic")
ks1 = kashti 
# # ks2 = kashti


kashti.head()


# simple operations (Math operations)
(kashti["age"]+12).head(10)

0    34.0
1    50.0
2    38.0
3    47.0
4    47.0
5     NaN
6    66.0
7    14.0
8    39.0
9    26.0
Name: age, dtype: float64


(kashti["age"]*2).head(3)

0    44.0
1    76.0
2    52.0
Name: age, dtype: float64


# where exactly missing value are ?
kashti.isnull().sum()

survived         0
pclass           0
sex              0
age            177
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
deck           688
embark_town      2
alive            0
alone            0
dtype: int64


# use a drop.na method 
print (kashti.shape)

(891, 15)


# removing missing value column 
# ks_clean= ks.drop (["deck"], axis= 1)
# ks_clean.head()


kashti.dropna(subset=["deck"], axis=0 , inplace = True)
kashti.shape

(203, 15)


kashti.isnull().sum()

survived        0
pclass          0
sex             0
age            19
sibsp           0
parch           0
fare            0
embarked        2
class           0
who             0
adult_male      0
deck            0
embark_town     2
alive           0
alone           0
dtype: int64


# removing na from whole dataframe
kashti = kashti.dropna()
kashti.isnull().sum()

survived       0
pclass         0
sex            0
age            0
sibsp          0
parch          0
fare           0
embarked       0
class          0
who            0
adult_male     0
deck           0
embark_town    0
alive          0
alone          0
dtype: int64


kashti.shape

(182, 15)


ks1.isnull().sum()

survived        0
pclass          0
sex             0
age            19
sibsp           0
parch           0
fare            0
embarked        2
class           0
who             0
adult_male      0
deck            0
embark_town     2
alive           0
alone           0
dtype: int64


# finding an average (mean)
mean = ks1["age"].mean()
mean

35.77945652173913


# replacing Nan with mean of the data (udating as well)
ks1["age"] = ks1["age"].replace(np.nan , mean)


ks1["age"].head(10)

1     38.000000
3     35.000000
6     54.000000
10     4.000000
11    58.000000
21    34.000000
23    28.000000
27    19.000000
31    35.779457
52    49.000000
Name: age, dtype: float64


ks1.isnull().sum()

survived       0
pclass         0
sex            0
age            0
sibsp          0
parch          0
fare           0
embarked       2
class          0
who            0
adult_male     0
deck           0
embark_town    2
alive          0
alone          0
dtype: int64


mode = ks1["deck"].mode()[0]
mode

'C'


ks1['deck'].fillna(mode , inplace=True)
ks1['deck'].head(12)

1     C
3     C
6     E
10    G
11    C
21    D
23    A
27    C
31    B
52    D
54    B
55    C
Name: deck, dtype: category
Categories (7, object): ['A', 'B', 'C', 'D', 'E', 'F', 'G']


ks1.dropna(subset=["embarked"], axis=0 , inplace = True)


ks1.isnull().sum()

survived       0
pclass         0
sex            0
age            0
sibsp          0
parch          0
fare           0
embarked       0
class          0
who            0
adult_male     0
deck           0
embark_town    0
alive          0
alone          0
dtype: int64


# know the data type and convert it into the know one 
kashti.dtypes

survived          int64
pclass            int64
sex              object
age             float64
sibsp             int64
parch             int64
fare            float64
embarked         object
class          category
who              object
adult_male         bool
deck           category
embark_town      object
alive            object
alone              bool
dtype: object


# use this method to convert datatype from one to another format 
kashti["survived"]= kashti["survived"].astype("float64")
kashti.dtypes

C:\Users\Epazz\AppData\Local\Temp/ipykernel_12924/2743140032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  kashti["survived"]= kashti["survived"].astype("float64")

survived        float64
pclass            int64
sex              object
age             float64
sibsp             int64
parch             int64
fare            float64
embarked         object
class          category
who              object
adult_male         bool
deck           category
embark_town      object
alive            object
alone              bool
dtype: object


# here we will convert age into days instead of years 
ks1["age"] = ks1["age"]*365
ks1["age"].head(8)

1     13870.0
3     12775.0
6     19710.0
10     1460.0
11    21170.0
21    12410.0
23    10220.0
27     6935.0
Name: age, dtype: float64


# always rename afterwards 
ks1.rename(columns={"age": "age in days"}, inplace=True)
ks1.head()


ks1["age in days"]= ks1["age in days"].astype("int64")
ks1.head()


ks1.head()


ks4= ks1[["age in days","fare"]]
ks4.head()


# simple feature scalling 
ks4["fare"]=  ks4["fare"]/ks4["fare"].max()
ks4["age in days"]=  ks4["age in days"]/ks4["age in days"].max()
ks4.head()

C:\Users\Epazz\AppData\Local\Temp/ipykernel_12924/607069502.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  ks4["fare"]=  ks4["fare"]/ks4["fare"].max()
C:\Users\Epazz\AppData\Local\Temp/ipykernel_12924/607069502.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  ks4["age in days"]=  ks4["age in days"]/ks4["age in days"].max()


# Min- Max method 
# x.new = (x.old - x.min) / (x.max- x.min)
ks4["fare"] = (ks4["fare"]-ks4["fare"].min())/ (ks4["fare"].max()-ks4["fare"].min())
ks4.head()

C:\Users\Epazz\AppData\Local\Temp/ipykernel_12924/4070571851.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  ks4["fare"] = (ks4["fare"]-ks4["fare"].min())/ (ks4["fare"].max()-ks4["fare"].min())


# z-score method 
# x.new = (x.old -x.mean) / x.std
ks4["fare"]  =(ks4["fare"]- ks4["fare"].mean()) / ks4["fare"].std()
ks4.head()

C:\Users\Epazz\AppData\Local\Temp/ipykernel_12924/3694817774.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  ks4["fare"]  =(ks4["fare"]- ks4["fare"].mean()) / ks4["fare"].std()


ks =sns.load_dataset("titanic")
ks["fare"].head()

0     7.2500
1    71.2833
2     7.9250
3    53.1000
4     8.0500
Name: fare, dtype: float64


# log transfromation
# x.new = np.log(x.old) 
ks["fare"] = np.log(ks["fare"])
ks["fare"].head()

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\arraylike.py:364: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)

0    1.981001
1    4.266662
2    2.070022
3    3.972177
4    2.085672
Name: fare, dtype: float64


kashti.head()


ks4["age in days"].shape

(201,)


# Creating bins 
bins = np.linspace(min(ks1["age in days"]), max(ks1["age in days"]), 4)
bins

array([  335.        ,  9956.66666667, 19578.33333333, 29200.        ])


age_groups = ["bachay", "jawan", "Boorhay"]
ks1["age in days"] = pd.cut(ks1["age in days"], bins, labels= age_groups ,include_lowest=True )
ks1["age in days"]

1        jawan
3        jawan
6      Boorhay
10      bachay
11     Boorhay
        ...   
871      jawan
872      jawan
879    Boorhay
887     bachay
889     bachay
Name: age in days, Length: 201, dtype: category
Categories (3, object): ['bachay' < 'jawan' < 'Boorhay']


ks4.head()


ks1.head()


ks5 = sns.load_dataset("titanic")


data = pd.get_dummies(ks5["sex"])


# Drop column 
ks5 = ks5.drop('sex',axis = 1)
# Join 
ks5 = ks5.join(data)
ks5.head()


from scipy.stats import shapiro 
data = [0.873, 2.817 , 0.121, -0.945, -0.055, -1.436, 0.360 , -1.478, -1.637, -1.869]
shapiro(data)
#here we see that the p value is greater than 0.05 so the data is normally distributed

ShapiroResult(statistic=0.8951009511947632, pvalue=0.19340917468070984)


from scipy.stats import shapiro 
data = [0.873, 2.817 , 0.121, -0.945, -0.055, -1.436, 0.360 , -1.478, -1.637, -1.869]
stat, p=shapiro(data)
print("stat =",stat)
print("p =" ,p)

stat = 0.8951009511947632
p = 0.19340917468070984


# To write it respectively 
from scipy.stats import shapiro 
data = [0.873, 2.817 , 0.121, -0.945, -0.055, -1.436, 0.360 , -1.478, -1.637, -1.869]
stat, p=shapiro(data)
print ("stat=%3f, p= %3f" %(stat, p))
# print("stat =",stat)
# print("p =" ,p)

stat=0.895101, p= 0.193409


#example of the Shapiro-WIlk Normality test 
from scipy.stats import shapiro 
data = [0.873, 2.817 , 0.121, -0.945, -0.055, -1.436, 0.360 , -1.478, -1.637, -1.869]
stat, p=shapiro(data)
print ("stat=%3f, p= %3f" %(stat, p))

if p> 0.05:
    print("probability Gaussian")
else: 
    print ("Probability not gaussian")

stat=0.895101, p= 0.193409
probability Gaussian


import pandas as pd 
import seaborn as sns 
import matplotlib.pyplot as plt 
import numpy as np


ks = sns.load_dataset("titanic")
sns.boxplot(ks["age"])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='age'>


ks["age"].hist()

<AxesSubplot:>


# Normality test 
from scipy.stats import shapiro 
stat, p=shapiro(ks["age"])
print("stat =",stat)
print("p =" ,p)

if p> 0.05:
    print("probability Gaussian or the data is normal ")
else: 
    print ("Probability not gaussian or the data is not normal ")

stat = nan
p = 1.0
probability Gaussian or the data is normal


from scipy.stats import shapiro 
ks =ks.dropna()
stat, p=shapiro(ks["age"])

print("stat =",stat)
print("p =" ,p)

if p> 0.05:
    print("probability Gaussian or the data is normal ")
else: 
    print ("Probability not gaussian or the data is not normal ")

stat = 0.9906661510467529
p = 0.28414419293403625
probability Gaussian or the data is normal


sns.boxplot(ks["age"])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='age'>


from scipy.stats import shapiro 
ks =ks.dropna()
stat, p=shapiro(ks["fare"])

print("stat =",stat)
print("p =" ,p)

if p> 0.05:
    print("probability Gaussian or the data is normal ")
else: 
    print ("Probability not gaussian or the data is not normal ")

stat = 0.7430529594421387
p = 1.6486953687823121e-16
Probability not gaussian or the data is not normal


sns.boxplot(ks["fare"])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='fare'>


ks["fare"].hist()

<AxesSubplot:>


# Example of the Pearson's Correlation test
from scipy.stats import pearsonr
data1 = [0.873, 2.817, 0.121, -0.945, -0.055, -1.436, 0.360, -1.478, -1.637, -1.869]
data2 = [0.353, 3.517, 0.125, -7.545, -0.555, -1.536, 3.350, -1.578, -3.537, -1.579]
stat, p = pearsonr(data1, data2)
print('stat=%.3f, p=%.3f' % (stat, p))
if p > 0.05:
  print('Probably independent(No correlation)')
else:
  print('Probably dependent (correlation exists)')

stat=0.688, p=0.028
Probably dependent (correlation exists)


ks1 = sns.load_dataset("titanic")


# Example of the Pearson's Correlation test
from scipy.stats import pearsonr
stat, p = pearsonr(ks1["age"], ks1["fare"])
print('stat=%.3f, p=%.3f' % (stat, p))
if p > 0.05:
  print('Probably independent')
else:
  print('Probably dependent')
  # here there is error as the array must not contain Nan values so we should drop them first

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_892/996795437.py in <module>
      1 # Example of the Pearson's Correlation test
      2 from scipy.stats import pearsonr
----> 3 stat, p = pearsonr(ks1["age"], ks1["fare"])
      4 print('stat=%.3f, p=%.3f' % (stat, p))
      5 if p > 0.05:

~\AppData\Local\Programs\Python\Python310\lib\site-packages\scipy\stats\stats.py in pearsonr(x, y)
   4043     # scipy.linalg.norm(xm) does not overflow if xm is, for example,
   4044     # [-5e210, 5e210, 3e200, -3e200]
-> 4045     normxm = linalg.norm(xm)
   4046     normym = linalg.norm(ym)
   4047 

~\AppData\Local\Programs\Python\Python310\lib\site-packages\scipy\linalg\misc.py in norm(a, ord, axis, keepdims, check_finite)
    143     # Differs from numpy only in non-finite handling and the use of blas.
    144     if check_finite:
--> 145         a = np.asarray_chkfinite(a)
    146     else:
    147         a = np.asarray(a)

~\AppData\Local\Programs\Python\Python310\lib\site-packages\numpy\lib\function_base.py in asarray_chkfinite(a, dtype, order)
    601     a = asarray(a, dtype=dtype, order=order)
    602     if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
--> 603         raise ValueError(
    604             "array must not contain infs or NaNs")
    605     return a

ValueError: array must not contain infs or NaNs


# Example of the Pearson's Correlation test
from scipy.stats import pearsonr
ks1= ks1.dropna()
stat, p = pearsonr(ks1["age"], ks1["fare"])
print('stat=%.3f, p=%.3f' % (stat, p))
if p > 0.05:
  print('Probably independent')
else:
  print('Probably dependent')

stat=-0.091, p=0.223
Probably independent


# Example of the Student's t-test
from scipy.stats import ttest_ind
data1 = [0.873, 2.817, 0.121, -0.945, -0.055, -1.436, 0.360, -1.478, -1.637, -1.869]
data2 = [1.142, -0.432, -0.938, -0.729, -0.846, -0.157, 0.500, 1.183, -1.075, -0.169]
stat, p = ttest_ind(data1, data2)
print('stat=%.3f, p=%.3f' % (stat, p))
if p > 0.05:
  print('Probably the same dataset')
else:
  print('Probably different data set')

stat=-0.326, p=0.748
Probably the same dataset


# Example of the Paired Student's t-test
from scipy.stats import ttest_rel
data1 = [0.873, 2.817, 0.121, -0.945, -0.055, -1.436, 0.360, -1.478, -1.637, -1.869]
data2 = [1.142, -0.432, -0.938, -0.729, -0.846, -0.157, 0.500, 1.183, -1.075, -0.169]
stat, p = ttest_rel(data1, data2)
print('stat=%.3f, p=%.3f' % (stat, p))
if p > 0.05:
  print('Probably the same distribution')
else:
  print('Probably different distributions')

stat=-0.334, p=0.746
Probably the same distribution


# Example of the Analysis of Variance test 
from scipy.stats import f_oneway
data1 = [0.873, 2.817, 0.121, -0.945, -0.055, -1.436, 0.360, -1.478, -1.637, -1.869]
data2 = [1.142, -0.432, -0.938, -0.729, -0.846, -0.157, 0.500, 1.183, -1.075, -0.169]
data3 = [-0.208, 0.696, 0.928, -1.148, -0.213, 0.229, 0.137, 0.269, -0.870, -1.204]
stat, p = f_oneway(data1, data2, data3)
print('stat=%.3f, p=%.3f' % (stat, p))
if p > 0.05:
  print('Probably the same distribution')
else:
  print('Probably different distributions')

stat=0.096, p=0.908
Probably the same distribution


# Some other test 
# Post hoc tests
#  - tuckey test
#  - Bonfroni test 
# Two way ANOVA
# MANOVA
# MANCOVA


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
df = pd.read_csv('salary_data.csv')
df.head()


X = df[["YearsExperience"]]
y = df["Salary"]
X.head()


y.head()

0    39343
1    46205
2    37731
3    43525
4    39891
Name: Salary, dtype: int64


#import library 
from sklearn.model_selection import train_test_split 
# slip the data 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)


# use least square method to fit the line 
from sklearn.linear_model import LinearRegression
linreg = LinearRegression()
model = LinearRegression().fit(X_train, y_train)
model

LinearRegression()


plt.scatter (X_train, y_train)
plt.plot(X_train, model.predict(X_train))
#plt.plot() ==> plot kro ,  plt.plot(X_train)==> X_train me , plt.plot(X_train)
# model.predict(X_train)) model ko pridict kro X_train se

[<matplotlib.lines.Line2D at 0x1dd06e01ab0>]


# Adding Colour 
plt.scatter (X_train, y_train)
plt.plot(X_train, model.predict(X_train) ,color = "Green")

[<matplotlib.lines.Line2D at 0x1dd06e84310>]


#Adding labels
plt.scatter (X_train, y_train)
plt.plot(X_train, model.predict(X_train) ,color = "Green")
plt.xlabel("Tajurba")
plt.ylabel("Tankhwah")
plt.title("Train Plot")
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


# Pridicting testing data 
plt.scatter (X_test, y_test)
plt.plot(X_train, model.predict(X_train) ,color = "red")
plt.xlabel("Tajurba")
plt.ylabel("Tankhwah")
plt.title("Test Plot")
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


# Model Fitness
# Jo library import ki hui hai usi k andr hi hum ne test score/regression score nikalna hai 
print("Score for testing data =", model.score(X_test, y_test))
print("Score for training data =", model.score(X_train, y_train))

Score for testing data = 0.988169515729126
Score for training data = 0.9411949620562126


model.score(X_test, y_test)

0.988169515729126


model.score(X_train, y_train)

0.9411949620562126


model.predict([[5]])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(

array([73342.97478427])


model.predict(X_test)

array([ 40748.96184072, 122699.62295594,  64961.65717022,  63099.14214487,
       115249.56285456, 107799.50275317])


model.predict([[5],[6],[10]])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(

array([ 73342.97478427,  82655.549911  , 119905.85041792])


a = ([[10], [20], [30], [5]])


model.predict(a)

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(

array([119905.85041792, 213031.60168521, 306157.3529525 ,  73342.97478427])


y_pred = model.predict(X_test)
y_pred

array([ 40748.96184072, 122699.62295594,  64961.65717022,  63099.14214487,
       115249.56285456, 107799.50275317])


x_new = np.linspace(0, 30, 100)
y_new = model.predict(x_new[:, np.newaxis])
y_new

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(

array([ 26780.09915063,  29602.09161327,  32424.08407592,  35246.07653856,
        38068.06900121,  40890.06146385,  43712.0539265 ,  46534.04638914,
        49356.03885179,  52178.03131444,  55000.02377708,  57822.01623973,
        60644.00870237,  63466.00116502,  66287.99362766,  69109.98609031,
        71931.97855295,  74753.9710156 ,  77575.96347824,  80397.95594089,
        83219.94840353,  86041.94086618,  88863.93332882,  91685.92579147,
        94507.91825411,  97329.91071676, 100151.9031794 , 102973.89564205,
       105795.88810469, 108617.88056734, 111439.87302998, 114261.86549263,
       117083.85795527, 119905.85041792, 122727.84288057, 125549.83534321,
       128371.82780586, 131193.8202685 , 134015.81273115, 136837.80519379,
       139659.79765644, 142481.79011908, 145303.78258173, 148125.77504437,
       150947.76750702, 153769.75996966, 156591.75243231, 159413.74489495,
       162235.7373576 , 165057.72982024, 167879.72228289, 170701.71474553,
       173523.70720818, 176345.69967082, 179167.69213347, 181989.68459611,
       184811.67705876, 187633.66952141, 190455.66198405, 193277.6544467 ,
       196099.64690934, 198921.63937199, 201743.63183463, 204565.62429728,
       207387.61675992, 210209.60922257, 213031.60168521, 215853.59414786,
       218675.5866105 , 221497.57907315, 224319.57153579, 227141.56399844,
       229963.55646108, 232785.54892373, 235607.54138637, 238429.53384902,
       241251.52631166, 244073.51877431, 246895.51123695, 249717.5036996 ,
       252539.49616224, 255361.48862489, 258183.48108754, 261005.47355018,
       263827.46601283, 266649.45847547, 269471.45093812, 272293.44340076,
       275115.43586341, 277937.42832605, 280759.4207887 , 283581.41325134,
       286403.40571399, 289225.39817663, 292047.39063928, 294869.38310192,
       297691.37556457, 300513.36802721, 303335.36048986, 306157.3529525 ])


from sklearn.metrics import r2_score
r2_score(y_test, y_pred)

0.988169515729126


from sklearn import metrics
print (metrics.mean_absolute_error(y_test, y_pred))
print(metrics.mean_squared_error(y_test, y_pred))
print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
# RAE
# RSE

2446.1723690465055
12823412.298126549
3580.979237321343


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


ins= pd.read_csv("insurance.csv")


#looking at first 5 rows of data set 
ins.head(5)


#listing the column names 
ins.columns

Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')


#Checking total no. of rows and coloumns
ins.shape

(1338, 7)


#overall info of data set , which type of data we have 
ins.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1338 entries, 0 to 1337
Data columns (total 7 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       1338 non-null   int64  
 1   sex       1338 non-null   object 
 2   bmi       1338 non-null   float64
 3   children  1338 non-null   int64  
 4   smoker    1338 non-null   object 
 5   region    1338 non-null   object 
 6   charges   1338 non-null   float64
dtypes: float64(2), int64(2), object(3)
memory usage: 73.3+ KB


ins.isnull().sum()

age         0
sex         0
bmi         0
children    0
smoker      0
region      0
charges     0
dtype: int64


plt.figure(figsize=(10,6)) # Setting the size of required figure 
sns.distplot(ins['charges'])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='charges', ylabel='Density'>


plt.figure(figsize=(10,6))
sns.boxplot(ins["charges"])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='charges'>


plt.figure(figsize=(10,6))
sns.distplot(ins['age'])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='age', ylabel='Density'>


plt.figure(figsize=(10,6))
sns.boxplot(ins["age"])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='age'>


plt.figure(figsize=(10,6))
sns.distplot(ins['bmi'])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='bmi', ylabel='Density'>


plt.figure(figsize=(10,6))
sns.boxplot(ins["bmi"])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='bmi'>


fig = plt.figure(figsize=(10,6))
sns.heatmap(ins.corr())

<AxesSubplot:>


# with values
fig = plt.figure(figsize=(10,6))
sns.heatmap(ins.corr(), annot=True)

<AxesSubplot:>


sns.lineplot(x= "age", y= "charges" , data = ins )

<AxesSubplot:xlabel='age', ylabel='charges'>


sns.lineplot(x= "bmi", y= "charges" , data = ins )

<AxesSubplot:xlabel='bmi', ylabel='charges'>


sns.scatterplot(x='bmi', y='charges', data=ins )

<AxesSubplot:xlabel='bmi', ylabel='charges'>


sns.jointplot(x='bmi', y='charges', data=ins ,  kind='hex')

<seaborn.axisgrid.JointGrid at 0x1dd0a4ec5e0>


ins["sex"].unique()

array(['female', 'male'], dtype=object)


ins["sex"]= ins["sex"].replace("male", 1)
ins["sex"]= ins["sex"].replace("female", 0)


ins["smoker"].unique()

array(['yes', 'no'], dtype=object)


ins["smoker"]= ins["smoker"].replace("yes", 1)
ins["smoker"]= ins["smoker"].replace("no", 0)


ins["region"].unique()

array(['southwest', 'southeast', 'northwest', 'northeast'], dtype=object)


# creating dummies for region coloumn 
r_dummies = pd.get_dummies(ins["region"])


# Drop column 
ins = ins.drop('region',axis = 1)
# Join 
ins = ins.join(r_dummies)
ins.head()


# now checking the correlation 
fig = plt.figure(figsize=(12,6))
sns.heatmap(ins.corr(), annot=True)

<AxesSubplot:>


X= ins[["age","bmi", "smoker"]]
y= ins["charges"]


# importing Linear regression algorithm from sklearn library 
from sklearn.linear_model import LinearRegression
# fitting our X and y values in this algorithm 
model = LinearRegression().fit(X,y)
model

LinearRegression()


# getting prediction in following way 
#model.predict([[age  ,bmi , smoker]])
model.predict([[19 ,27.9, 1]])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(

array([26079.21861526])


#Cheching model co-efficient and intercept 
print(model.coef_)
print(model.intercept_)

[  259.54749155   322.61513282 23823.68449531]
-11676.830425187778


coeff_ins = pd.DataFrame(model.coef_,X.columns,columns=['Co-efficient'])
coeff_ins


#import library 
from sklearn.model_selection import train_test_split 
# splitting the data : 80% training data and 20% testing data  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)


# fitting the training data in linear regression again 
from sklearn.linear_model import LinearRegression
model1 = LinearRegression().fit(X_train, y_train)
model1

LinearRegression()


# getting a prediction again 
model1.predict([[19 ,27.9, 1]])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(

array([25916.43448944])


# getting prediction for whole values we splitted for test 
y_pred = model1.predict(X_test)


#importing library for test  
from sklearn.metrics import r2_score
# now testing between already know y_test values and y_pred values 
score = r2_score(y_test, y_pred)
score

0.7945500805653087


from sklearn import metrics
print('MAE:', metrics.mean_absolute_error(y_test, y_pred))
print('MSE:', metrics.mean_squared_error(y_test, y_pred))
print('RMSE', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

MAE: 3960.8666198087158
MSE: 32693237.938726168
RMSE 5717.80009607945


age = input('What is your age ? \n')
bmi = input('What is your bmi  ? \n')
smoker= input('press 1 if you are  a smoker and 0 if not \n')
try:
    print('We predict {:.0f}$ will be your insurance cost .'.format(
        model.predict([[float(age), float(bmi),int(smoker)]])[0]))
except ValueError:
    print('Please only input either:\n- whole number e.g. 1, 4, 7\n- decimal/float number e.g. 3.8')

We predict 74168$ will be your insurance cost .

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn import linear_model
from sklearn.linear_model import LinearRegression

# Importing the dataset
df = pd.read_csv('ml_data_salary.csv')
df.head()


# Set independent and dependent variables
X = df[['distance', 'YearsExperience']]
y = df['Salary']


# Initialize model from sklearn and fit it into our data
regr = linear_model.LinearRegression()
model = regr.fit(X, y)
model

LinearRegression()


print('Intercept:', model.intercept_)
print('Coefficients:', model.coef_)

Intercept: -218603.37708034192
Coefficients: [3258.60769705 1303.44307882]


# Values to predict
distance = input('How much distance you have to cover for job? \n')
YearsExperience = input('How many years of experience do you have ? \n')

try:
    print('We predict {:.0f}$ will be you salary if you have to cover {}m and have workin experience of {} years.'.format(
        model.predict([[float(distance), float(YearsExperience)]])[0],
        distance, 
        YearsExperience))
except ValueError:
    print('Please only input either:\n- whole number e.g. 1, 4, 7\n- decimal/float number e.g. 3.8')

We predict 37360$ will be you salary if you have to cover 77.75m and have workin experience of 2 years.

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(


X.shape

(30, 2)


# Prepare data
X = df[['distance', 'YearsExperience']].values.reshape(-1,2)
Y = df['Salary']


print(df["distance"].max())
print(df["distance"].min())

101.25
77.75


print(df["YearsExperience"].max())
print(df["YearsExperience"].min())

10.5
1.1


# Create range for each dimension
x = X[:, 0]
y = X[:, 1]
z = Y


xx_pred = np.linspace(77, 102, 30)  # range of distance values
yy_pred = np.linspace(1, 11, 30)  # range of YearsExperience values
xx_pred, yy_pred = np.meshgrid(xx_pred, yy_pred)
model_viz = np.array([xx_pred.flatten(), yy_pred.flatten()]).T


predicted = model.predict(model_viz)

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(


# Evaluate model by using it's R^2 score 
r2 = model.score(X, Y)
r2
#r2 score is almost near to one so we are good to go

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(

0.9569566641435084


# Plot model visualization
plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(12, 4))

ax1 = fig.add_subplot(131, projection='3d')
ax2 = fig.add_subplot(132, projection='3d')
ax3 = fig.add_subplot(133, projection='3d')

axes = [ax1, ax2, ax3]

for ax in axes:
    ax.plot(x, y, z, color='k', zorder=15, linestyle='none', marker='o', alpha=0.5)
    ax.scatter(xx_pred.flatten(), yy_pred.flatten(), predicted, facecolor=(0,0,0,0), s=20, edgecolor='#70b3f0', )
    ax.set_xlabel('distance', fontsize=12)
    ax.set_ylabel('YearsExperience', fontsize=12)
    ax.set_zlabel('Salary', fontsize=12)
    ax.locator_params(nbins=4, axis='x')
    ax.locator_params(nbins=5, axis='x')

    ax1.view_init(elev=25, azim=-60)
ax2.view_init(elev=15, azim=15)
ax3.view_init(elev=25, azim=60)

fig.suptitle('Multi-Linear Regression Model Visualization ($R^2 = %.2f$)' % r2, fontsize=16, color='k')

fig.tight_layout()


#FOR accuracy 
#import library 
from sklearn.model_selection import train_test_split 
X = df[["distance", "YearsExperience","age"]]
y = df["Salary"]
# slip the data 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)


from sklearn.linear_model import LinearRegression
linreg = LinearRegression()
model = LinearRegression().fit(X_train, y_train)
model

LinearRegression()


plt.scatter (X_train[["distance"]], y_train)
plt.scatter (X_train[["YearsExperience"]], y_train)
plt.scatter (X_train[["age"]], y_train)
plt.plot(X_train, model.predict(X_train), color = "Grey" )
plt.xlabel("YearsExperience| age | distance")
plt.ylabel("Salary")
plt.title("Train Plot ")
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


#R2 test 
print("Score for testing data =", model.score(X_test, y_test))
print("Score for training data =", model.score(X_train, y_train))

Score for testing data = 0.9880925772756097
Score for training data = 0.9411691490005899


model.predict([[33 ,77.75, 1.1]])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(

array([-5.09866629e+17])


y_pred =model.predict(X_test)
y_pred

array([ 40784., 122688.,  64992.,  63120., 115264., 107840.])


from sklearn import metrics
print (metrics.mean_absolute_error(y_test, y_pred))

2442.5


print(metrics.mean_squared_error(y_test, y_pred))

12906808.166666666


print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

3592.604649368848


# Importing libraries
# Pandas is used for data manipulation
import pandas as pd
# Read in data and display first 5 rows
df = pd.read_csv('temps.csv')
df.head(5)


#Checking the shape of our data that how many row and coloumns we hai 
print('The shape of our features is:', df.shape)
#so we have 348 rows and 12 coloumns in our data

The shape of our features is: (348, 12)


# check if there is Nan values in here our data
df.isnull().sum()
#there is no Nan value in our data

year              0
month             0
day               0
week              0
temp_2            0
temp_1            0
average           0
actual            0
forecast_noaa     0
forecast_acc      0
forecast_under    0
friend            0
dtype: int64


# getting some information of our data 
df.info()
# here we have one catagorical value column . 
# As we are working in regression so we have to convert it into integer type by making the dummy data

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 348 entries, 0 to 347
Data columns (total 12 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   year            348 non-null    int64  
 1   month           348 non-null    int64  
 2   day             348 non-null    int64  
 3   week            348 non-null    object 
 4   temp_2          348 non-null    int64  
 5   temp_1          348 non-null    int64  
 6   average         348 non-null    float64
 7   actual          348 non-null    int64  
 8   forecast_noaa   348 non-null    int64  
 9   forecast_acc    348 non-null    int64  
 10  forecast_under  348 non-null    int64  
 11  friend          348 non-null    int64  
dtypes: float64(1), int64(10), object(1)
memory usage: 32.8+ KB


# Converting the catagorical values data using pandas get_dummies
df = pd.get_dummies(df)
# Display the first 5 rows of the last 12 columns
df.iloc[:,5:].head(5)


# again checking the shape of our data 
df.shape

(348, 18)


# Use numpy to convert to arrays
import numpy as np
# Labels are the values we want to predict
labels = np.array(df['actual'])

# Remove the labels from the features
# axis 1 refers to the columns
df= df.drop('actual',axis = 1)
df= df.drop("forecast_noaa",axis = 1)
#'forecast_under','friend', 'week_Fri''week_Mon','week_Sat','week_Sun','week_Thurs','week_Tues','week_Wed')
# Convert to numpy array
features = np.array(df)


# Saving feature names for later use
df_list = list(df.columns)
df_list

['year',
 'month',
 'day',
 'temp_2',
 'temp_1',
 'average',
 'forecast_acc',
 'forecast_under',
 'friend',
 'week_Fri',
 'week_Mon',
 'week_Sat',
 'week_Sun',
 'week_Thurs',
 'week_Tues',
 'week_Wed']


# Using Skicit-learn to split data into training and testing sets
from sklearn.model_selection import train_test_split
# Split the data into training and testing sets
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25, random_state = 42)


print('Training Features Shape:', train_features.shape)
print('Training Labels Shape:', train_labels.shape)
print('Testing Features Shape:', test_features.shape)
print('Testing Labels Shape:', test_labels.shape)

Training Features Shape: (261, 16)
Training Labels Shape: (261,)
Testing Features Shape: (87, 16)
Testing Labels Shape: (87,)


# Import the model we are using
from sklearn.ensemble import RandomForestRegressor
# Instantiate model with 1000 decision trees
rf = RandomForestRegressor(n_estimators = 1000, random_state = 42)
# Train the model on training data
rf.fit(train_features, train_labels)

RandomForestRegressor(n_estimators=1000, random_state=42)


# Use the forest's predict method on the test data
predictions = rf.predict(test_features)


#Checking score of testing and training data 
print("Score for testing data =", rf.score(test_features, test_labels))
print("Score for training data =", rf.score(train_features, train_labels))

Score for testing data = 0.8149088174655048
Score for training data = 0.9746126233418512


# Checking the mean absolute error 
from sklearn import metrics
print (metrics.mean_absolute_error(test_labels, predictions))

# Long way to do which we have done with sklearn library
# # Calculate the absolute errors
# errors = abs(predictions - test_labels)
# # Print out the mean absolute error (mae)
# print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')

3.859586206896551


# Import tools needed for visualization
from sklearn.tree import export_graphviz
import pydot

# Pull out one tree from the forest
tree = rf.estimators_[5]
tree

DecisionTreeRegressor(max_features='auto', random_state=1201263687)


# Export the image to a dot file
export_graphviz(tree, out_file = 'tree.dot', feature_names = df_list, rounded = True, precision = 1)


# Use dot file to create a graph
(graph, ) = pydot.graph_from_dot_file('tree.dot')


# Write graph to a png file
graph.write_png('tree.png')


# Limit depth of tree to 3 levels and 10 trees 
rf_small = RandomForestRegressor(n_estimators=10, max_depth = 3)
rf_small.fit(train_features, train_labels)

RandomForestRegressor(max_depth=3, n_estimators=10)


# Extract the small tree
tree_small = rf_small.estimators_[5]


# Save the tree as a png image
export_graphviz(tree_small, out_file = 'small_tree.dot', feature_names = df_list, rounded = True, precision = 1)
(graph, ) = pydot.graph_from_dot_file('small_tree.dot')
graph.write_png('small_tree.png')


# for checking a polynomial regression lets firstle check what is bad fit 
# bad fit 
import numpy as np
import matplotlib.pyplot as plt 

x = [89,43,36,36,95,10,66,34,38,20,26,29,48,64,6,5,36,66,72,40]
y = [21,46,3,35,67,95,53,72,58,10,26,34,90,33,38,20,56,2,47,15]

mymodel= np.poly1d(np.polyfit(x ,y ,3))
myline= np.linspace(2 , 95, 100)

plt.scatter(x,y)
plt.plot(myline, mymodel(myline))
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


# R Square test for bad fit
from sklearn.metrics import r2_score 

x = [89,43,36,36,95,10,66,34,38,20,26,29,48,64,6,5,36,66,72,40]
y = [21,46,3,35,67,95,53,72,58,10,26,34,90,33,38,20,56,2,47,15]
model = np.poly1d(np.polyfit(x,y,3))

print(r2_score(y, model(x)))

0.009952707566680652


# R Square test for bad fit
from sklearn.metrics import r2_score 

x = [89,43,36,36,95,10,66,34,38,20,26,29,48,64,6,5,36,66,72,40]
y = [21,46,3,35,67,95,53,72,58,10,26,34,90,33,38,20,56,2,47,15]
model = np.poly1d(np.polyfit(x,y,3))

print(r2_score(y, model(x)))

0.009952707566680652


# Step2 : Draw a line 
import numpy as np
import matplotlib.pyplot as plt 
x= [1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,21,22]
y= [100,80,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]

mymodel= np.poly1d(np.polyfit(x ,y ,3))
myline= np.linspace(1 , 22, 200)

plt.scatter(x,y)
plt.plot(myline, mymodel(myline))
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


# Step 3 : R-Squared 
import numpy as np
import matplotlib.pyplot as plt 
x= [1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,21,22]
y= [100,80,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]

model = np.poly1d(np.polyfit(x,y,3))

print(r2_score(y, model(x)))

0.9338713637130449


# Prediction 

import numpy as np
import matplotlib.pyplot as plt 
x= [1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,21,22]
y= [100,80,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]

model = np.poly1d(np.polyfit(x,y,3))

speed = model(10)
speed

62.60788989976389


# Importing Libraries 
import numpy as np
import matplotlib.pyplot as plt 
import pandas as pd 

# IMporting the dataset 
dataset= pd.read_csv("https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/position_salaries.csv")
dataset.head()


X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values


# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)


# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X,y)

# Visualizing the Linear Regression results
def viz_linear():
    plt.scatter(X, y, color="red")
    plt.plot(X, lin_reg.predict(X), color="blue")
    plt.title("Truth or Bluff (Linear Regression)")
    plt.xlabel("Position level")
    plt.ylabel("Salary")
    plt.show()
    return
viz_linear()


# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=4)
X_poly = poly_reg.fit_transform(X)
pol_reg = LinearRegression()
pol_reg.fit(X_poly, y)

LinearRegression()


# Visualizing the Polymonial Regression results
def viz_polymonial():
    plt.scatter(X, y, color="red")
    plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color="blue")
    plt.title("Truth or Bluff (Linear Regression)")
    plt.xlabel("Position level")
    plt.ylabel("Salary")
    plt.show()
    return
viz_polymonial()


# Predicting a new result with Polymonial Regression
Pred_linear = lin_reg.predict([[11]])


# Predicting a new result with Polymonial Regression
Pred_ploynomial = pol_reg.predict(poly_reg.fit_transform([[11]]))


print('Linear Regression Results =', Pred_linear)
print('Polynomial Regression Results =' ,Pred_ploynomial )
print("The differene is ", Pred_ploynomial - Pred_linear)

Linear Regression Results = [694333.33333333]
Polynomial Regression Results = [1780833.33333359]
The differene is  [1086500.00000025]


#import libraries
import pandas as pd 
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


#import online data 
from sklearn.datasets import load_digits


digits= load_digits()


# input variables  or features (X)
digits.data.shape
X = digits.data
# means 1797 pictures size is 64 (8x8)


# output labels (y)
digits.target.shape
y= digits.target


plt.gray()
plt.matshow(digits.images[520])

<matplotlib.image.AxesImage at 0x1ca6e21fd60>

<Figure size 432x288 with 0 Axes>


plt.figure(figsize=(20,4))
for index, (image, label) in enumerate(zip(digits.data[0:5], digits.target[0:5])):
    plt.subplot(1,5, index+1)
    #using image(8,8) because we've seen the picsize of 64 i.e 8x8
    plt.imshow(np.reshape(image,(8,8)), cmap= plt.cm.gray)
    plt.title("Training : %i\n" % label, fontsize = 20)


plt.figure(figsize=(20,4))
for index, (image, label) in enumerate(zip(digits.data[0:10], digits.target[0:10])):
    plt.subplot(1,10, index+1)
    #using image(8,8) because we've seen the picsize of 64 i.e 8x8
    plt.imshow(np.reshape(image,(8,8)), cmap= plt.cm.gray)
    plt.title("Training : %i\n" % label, fontsize = 20)


plt.figure(figsize=(20,4))
for index, (image, label) in enumerate(zip(digits.data[0:10], digits.target[0:10])):
    plt.subplot(1,10, index+1)
    #using image(8,8) because we've seen the picsize of 64 i.e 8x8
    plt.imshow(np.reshape(image,(8,8)), cmap= plt.cm.gray)
    plt.title(label, fontsize = 20)


#help(plt)


#split the data 
from sklearn.model_selection import train_test_split
X_train, X_test, y_train,y_test = train_test_split (X, y, test_size=0.25  ,random_state= 0)


print("Train input data : " , X_train.shape)
print("Test input data : " , X_test.shape)
print("Train output data : " , y_train.shape)
print("Test output data : " , y_test.shape)

Train input data :  (1347, 64)
Test input data :  (450, 64)
Train output data :  (1347,)
Test output data :  (450,)


# training a model 
from sklearn.linear_model import LogisticRegression
model = LogisticRegression().fit(X_train,y_train)
model

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

LogisticRegression()


print(X_test[0:10].shape)

(10, 64)


# prediction 
model.predict(X_test[0:10])

array([2, 8, 2, 6, 6, 7, 1, 9, 8, 5])


y_pred = model.predict(X_test)
y_pred.shape

(450,)


# Accuracy score 
score= model.score(X_test, y_test)
score 
# yha pe X_test ko as an input leta hai pr X_test ki prediction ko , y_test se compare kr k k score bta rha

0.9511111111111111


# Confusion matrix 
from sklearn import metrics
cm= metrics.confusion_matrix(y_test, y_pred)
cm

array([[37,  0,  0,  0,  0,  0,  0,  0,  0,  0],
       [ 0, 40,  0,  0,  0,  0,  0,  0,  2,  1],
       [ 0,  1, 40,  3,  0,  0,  0,  0,  0,  0],
       [ 0,  0,  0, 43,  0,  0,  0,  0,  1,  1],
       [ 0,  0,  0,  0, 37,  0,  0,  1,  0,  0],
       [ 0,  0,  0,  0,  0, 46,  0,  0,  0,  2],
       [ 0,  1,  0,  0,  0,  0, 51,  0,  0,  0],
       [ 0,  0,  0,  1,  1,  0,  0, 46,  0,  0],
       [ 0,  3,  1,  0,  0,  0,  0,  0, 43,  1],
       [ 0,  0,  0,  0,  0,  1,  0,  0,  1, 45]], dtype=int64)


#plotting a confusion metrix 
plt.figure(figsize=(10,10))
sns.heatmap(cm, annot=True, fmt=".3f", linewidths=0.5 , square= True, cmap= "Spectral")
plt.ylabel("Actual Output")
plt.xlabel("Predicted Output")
title = "Accuracy Score : {0}".format(score)
plt.title(title, size= 15)

Text(0.5, 1.0, 'Accuracy Score : 0.9511111111111111')


plt.figure(figsize=(9,9))
plt.imshow(cm, interpolation='nearest', cmap='Pastel1')
plt.title('Confusion matrix', size = 15)
plt.colorbar()
tick_marks = np.arange(10)
plt.xticks(tick_marks, ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], rotation=45, size = 10)
plt.yticks(tick_marks, ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], size = 10)
plt.tight_layout()
plt.ylabel('Actual label', size = 15)
plt.xlabel('Predicted label', size = 15)
width, height = cm.shape
for x in range (width):
 for y in range (height):
  plt.annotate(str(cm[x][y]), xy=(y, x), 
  horizontalalignment='center',
  verticalalignment='center')


# Getting a mis classified lables 
index= 0
misclassifiedIndex = []
for label, predict in zip(y_pred , y_test):
    if predict != label:
        misclassifiedIndex.append(index)
        print(label,predict,index)
    index +=1

9 5 56
4 7 94
1 6 118
1 8 124
5 9 130
9 8 169
9 5 181
1 8 196
8 1 213
3 2 235
3 2 251
7 4 315
9 1 325
8 1 331
2 8 335
8 3 378
1 8 398
1 2 415
3 7 425
8 9 429
9 3 430
3 2 440


#error is this 
# # Getting a mis classified lables 
# index= 0
# misclassifiedIndex = []
# for label, predict in zip(y_pred , y_test):
#     if predict != label:
#         misclassifiedIndex.append(index)
#         print(label,predict,index)
#         index +=1


# plotting missclassified label with know 
plt.figure(figsize= (20,5))
for plotIndex, badIndex in enumerate ( misclassifiedIndex[0:5]):
    plt.subplot(1, 5, plotIndex + 1)
    plt.imshow (np.reshape(X_test[badIndex],(8,8)), cmap= plt.cm.gray)
    plt.title("Predicted :{}, Actual : {}".format (y_pred[badIndex], y_test[badIndex]), fontsize=18)


import pandas as pd
df = pd.read_csv("mldata.csv")
df["gender"]= df["gender"].replace("Male", 1)
df["gender"]= df["gender"].replace("Female", 0)


X= df[["weight","gender"]]
y= df["likeness"]


#machine learning algorithm 
from sklearn.neighbors import KNeighborsClassifier

# create and fit our model 
model = KNeighborsClassifier(n_neighbors= 5).fit(X,y)
model

KNeighborsClassifier()


#prediction 
model.predict([[70, 1]])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names
  warnings.warn(

array(['Biryani'], dtype=object)


model.predict(X)

array(['Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Samosa', 'Samosa', 'Biryani', 'Biryani',
       'Samosa', 'Biryani', 'Biryani', 'Samosa', 'Biryani', 'Samosa',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Pakora', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Samosa', 'Biryani', 'Biryani', 'Biryani', 'Samosa',
       'Biryani', 'Pakora', 'Biryani', 'Biryani', 'Biryani', 'Pakora',
       'Biryani', 'Biryani', 'Biryani', 'Samosa', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Pakora', 'Biryani', 'Samosa',
       'Biryani', 'Pakora', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Pakora',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Samosa', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Samosa', 'Biryani', 'Biryani',
       'Samosa', 'Biryani', 'Biryani', 'Biryani', 'Pakora', 'Pakora',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Samosa', 'Pakora', 'Samosa', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Samosa', 'Biryani',
       'Biryani', 'Samosa', 'Biryani', 'Pakora', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Samosa', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Pakora', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Samosa', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Samosa', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Samosa', 'Biryani',
       'Biryani', 'Biryani', 'Samosa', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Samosa', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani'],
      dtype=object)


# split data into test and train (80/20 rule)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2 ,random_state= 0)

# create a model 
model = KNeighborsClassifier(n_neighbors=12)
# fittine a model 
model.fit(X_train, y_train)

predicted_values = model.predict(X_test)
predicted_values

array(['Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani', 'Biryani',
       'Biryani'], dtype=object)


#checkin score 
from sklearn.metrics import accuracy_score
score = accuracy_score(y_test, predicted_values)
score

0.6530612244897959


from sklearn.metrics import f1_score
score = f1_score(y_test, predicted_values,average='weighted')
score

0.5159989921894684


from sklearn.metrics import precision_score
score = precision_score(y_test, predicted_values ,average='macro')
score

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

0.21768707482993196


from sklearn.metrics import recall_score
score = recall_score(y_test, predicted_values,average='micro')
score

0.6530612244897959


# Descion tree classifier me hum do ya do se ziada parameters ko lene k baad hum class define krte hain
import pandas as pd
df = pd.read_csv("mldata.csv


df.head()


#catagorical value ko pehle change krna hota 
df["gender"]= df["gender"].replace("Male", 1)
df["gender"]= df["gender"].replace("Female", 0)
df.tail()


# catagorical variable ko define krne k liey decision tree classifier ka use krna parta hai


# selection of input and output variables
X= df[["weight","gender"]]
y= df["likeness"]


X.head()


y.head()

0    Biryani
1    Biryani
2    Biryani
3    Biryani
4    Biryani
Name: likeness, dtype: object


#machine learning algorithm 
from sklearn.tree import DecisionTreeClassifier

# create and fit our model 
model = DecisionTreeClassifier().fit(X,y)


#prediction 
model.predict([[80, 1]])

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
  warnings.warn(

array(['Biryani'], dtype=object)


model.predict([[23, 1]])[0]

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
  warnings.warn(

'Biryani'


# how to measure the accurary of our model 
# slit data into test and train (80/20 rule)
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2 , random_state= 0)

# create a model 
model = DecisionTreeClassifier()
# fittine a model 
model.fit(X_train, y_train)

predicted_values = model.predict(X_test)
predicted_values
#checkin score 
score = accuracy_score(y_test, predicted_values)
score

0.6122448979591837


import seaborn as sns 
import matplotlib.pyplot as plt 

sns.boxplot(x= X_train["weight"],hue= X_train["gender"] ,y= y_train, data= df)

<AxesSubplot:xlabel='weight', ylabel='likeness'>


# HOw to train and save your model 
import pandas as pd 
from sklearn.tree import DecisionTreeClassifier
import joblib

model = DecisionTreeClassifier().fit(X,y)

joblib.dump(model, "foodie.joblib")

['foodie.joblib']


#graph 
from sklearn import tree
model = DecisionTreeClassifier().fit(X,y)


#graph 
from sklearn import tree
model = DecisionTreeClassifier().fit(X,y)
# model.fit(X, y)
# graphic evaluation / look into what happened 
tree.export_graphviz(model,
                     out_file="foodie.dot",
                     feature_names=["age", "gender"],
                     class_names = sorted( y.unique()),
                     label= "all",
                     rounded= True,
                     filled =True)


#Load data set 
import pandas as pd 
import seaborn as sns 
import numpy as np

df1= sns.load_dataset("iris")
df1.head()


import matplotlib.pyplot as plt 
from sklearn.tree import DecisionTreeClassifier
# df1 mtlb ye wala data ..Iloc mtb kis type ka data chahiey us me se 
#then square bracket me pehle row ati and : ka mtlb k sari rows aa gai 
# then comma , and then column ate ..hum ne kha ki : mtlb sare column aa jey -1 last wale k ilawa 
X= df1.iloc[: , :-1] 
# and yhe pe -1 likha k sirf last wala column : mtlb baki sare column chor k 
y= df1.iloc[:, -1:]
# agr hum sif -1 likhe ge to wo us column ki values show kr de ga ..but humko us column ka index b chahiey


X.head()


y.head()


from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import plot_tree

model = DecisionTreeClassifier().fit(X,y)
model

DecisionTreeClassifier()


plot_tree(model , filled = True)
plt.title("Decision tree trained model of IRIS data")

# FOllowing codes are to sav
# #saving high resolution png 
# plt.savefig('saving-a-high-resolution-plot.png', dpi=300)
# #saving high resolution png with transparent
# plt.savefig('saving-a-plot-as-png-file-transparent.png',dpi=400, transparent=True)
# #saving a pdf file a with high resolution 
# plt.savefig('saving-a-plot-in-pdf.pdf', dpi=400)
# #saving a tiff file with high resolution 
# plt.savefig('saving-a-plot-as-tiff-file.tiff', dpi=500)

plt.show()


# load libraires adn data set
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 

df= sns.load_dataset("iris")
df.head()


X= df.iloc[: ,:-1]
y= df.iloc[:, -1:]


from sklearn.ensemble import RandomForestClassifier
#n_estimators default 100 
# The Number of trees in forest 
model = RandomForestClassifier(n_estimators=100)
model.fit(X,y)
model.predict([[5,4,2,6]])

C:\Users\Epazz\AppData\Local\Temp/ipykernel_10720/889084137.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  model.fit(X,y)
C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names
  warnings.warn(

array(['setosa'], dtype=object)


# IMporting library 
from sklearn.model_selection import train_test_split
# splitting the data 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
y_pred= model.predict(X_test)
y_pred

array(['setosa', 'virginica', 'versicolor', 'setosa', 'setosa', 'setosa',
       'versicolor', 'setosa', 'virginica', 'versicolor', 'virginica',
       'virginica', 'virginica', 'setosa', 'virginica', 'virginica',
       'versicolor', 'versicolor', 'versicolor', 'setosa', 'setosa',
       'versicolor', 'setosa', 'versicolor', 'versicolor', 'virginica',
       'virginica', 'versicolor', 'versicolor', 'setosa', 'virginica',
       'virginica', 'virginica', 'setosa', 'versicolor', 'setosa',
       'setosa', 'versicolor'], dtype=object)


# Accuracy test 
score = model.score(X_test, y_test)
score

1.0


from sklearn import metrics
cm = metrics.confusion_matrix(y_test, y_pred)
cm

array([[13,  0,  0],
       [ 0, 13,  0],
       [ 0,  0, 12]], dtype=int64)


#plotting a confusion metrix 
plt.figure(figsize=(10,10))
sns.heatmap(cm, annot=True, fmt=".3f", linewidths=0.5 , square= True, cmap= "Spectral")
plt.ylabel("Actual Output")
plt.xlabel("Predicted Output")
title = "Accuracy Score : {0}".format(score)
plt.title(title, size= 15)

Text(0.5, 1.0, 'Accuracy Score : 1.0')


# load libraires adn data set
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns


# Load data set from Seaborn library 
df= sns.load_dataset("iris")
df.head()


#Defining features and lables at X and y respectively 
X= df.iloc[: ,:-1]
y= df.iloc[:, -1:]


# Checking our variables 
print(X.head())
print(y.head())

   sepal_length  sepal_width  petal_length  petal_width
0           5.1          3.5           1.4          0.2
1           4.9          3.0           1.4          0.2
2           4.7          3.2           1.3          0.2
3           4.6          3.1           1.5          0.2
4           5.0          3.6           1.4          0.2
  species
0  setosa
1  setosa
2  setosa
3  setosa
4  setosa


# splitting X and y into training and testing sets at 80/20 rule 
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)


# training the model on training set
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(X_train, y_train)

C:\Users\Epazz\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\utils\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)

GaussianNB()


# making predictions on the testing set
y_pred = gnb.predict(X_test)


# comparing actual response values (y_test) with predicted response values (y_pred)
from sklearn import metrics
print("Gaussian Naive Bayes model accuracy(in %):", metrics.accuracy_score(y_test, y_pred)*100)

Gaussian Naive Bayes model accuracy(in %): 96.66666666666667


#Import scikit-learn dataset library
from sklearn import datasets
#Load dataset
cancer = datasets.load_breast_cancer()


type(cancer)

sklearn.utils.Bunch


# print the names of the 30 features
print("Features: ", cancer.feature_names)
# print the label type of cancer('malignant' "benign')
print("Labels: ", cancer.target_names)

Features:  ['mean radius' 'mean texture' 'mean perimeter' 'mean area'
 'mean smoothness' 'mean compactness' 'mean concavity'
 'mean concave points' 'mean symmetry' 'mean fractal dimension'
 'radius error' 'texture error' 'perimeter error' 'area error'
 'smoothness error' 'compactness error' 'concavity error'
 'concave points error' 'symmetry error' 'fractal dimension error'
 'worst radius' 'worst texture' 'worst perimeter' 'worst area'
 'worst smoothness' 'worst compactness' 'worst concavity'
 'worst concave points' 'worst symmetry' 'worst fractal dimension']
Labels:  ['malignant' 'benign']


# Print data (features) shape 
cancer.data.shape

(569, 30)


#print the cancer data features (top 5 records)
print(cancer.data[0:5])

[[1.799e+01 1.038e+01 1.228e+02 1.001e+03 1.184e-01 2.776e-01 3.001e-01
  1.471e-01 2.419e-01 7.871e-02 1.095e+00 9.053e-01 8.589e+00 1.534e+02
  6.399e-03 4.904e-02 5.373e-02 1.587e-02 3.003e-02 6.193e-03 2.538e+01
  1.733e+01 1.846e+02 2.019e+03 1.622e-01 6.656e-01 7.119e-01 2.654e-01
  4.601e-01 1.189e-01]
 [2.057e+01 1.777e+01 1.329e+02 1.326e+03 8.474e-02 7.864e-02 8.690e-02
  7.017e-02 1.812e-01 5.667e-02 5.435e-01 7.339e-01 3.398e+00 7.408e+01
  5.225e-03 1.308e-02 1.860e-02 1.340e-02 1.389e-02 3.532e-03 2.499e+01
  2.341e+01 1.588e+02 1.956e+03 1.238e-01 1.866e-01 2.416e-01 1.860e-01
  2.750e-01 8.902e-02]
 [1.969e+01 2.125e+01 1.300e+02 1.203e+03 1.096e-01 1.599e-01 1.974e-01
  1.279e-01 2.069e-01 5.999e-02 7.456e-01 7.869e-01 4.585e+00 9.403e+01
  6.150e-03 4.006e-02 3.832e-02 2.058e-02 2.250e-02 4.571e-03 2.357e+01
  2.553e+01 1.525e+02 1.709e+03 1.444e-01 4.245e-01 4.504e-01 2.430e-01
  3.613e-01 8.758e-02]
 [1.142e+01 2.038e+01 7.758e+01 3.861e+02 1.425e-01 2.839e-01 2.414e-01
  1.052e-01 2.597e-01 9.744e-02 4.956e-01 1.156e+00 3.445e+00 2.723e+01
  9.110e-03 7.458e-02 5.661e-02 1.867e-02 5.963e-02 9.208e-03 1.491e+01
  2.650e+01 9.887e+01 5.677e+02 2.098e-01 8.663e-01 6.869e-01 2.575e-01
  6.638e-01 1.730e-01]
 [2.029e+01 1.434e+01 1.351e+02 1.297e+03 1.003e-01 1.328e-01 1.980e-01
  1.043e-01 1.809e-01 5.883e-02 7.572e-01 7.813e-01 5.438e+00 9.444e+01
  1.149e-02 2.461e-02 5.688e-02 1.885e-02 1.756e-02 5.115e-03 2.254e+01
  1.667e+01 1.522e+02 1.575e+03 1.374e-01 2.050e-01 4.000e-01 1.625e-01
  2.364e-01 7.678e-02]]


# print the cancer labels (0:malignant, 1:benign)
print(cancer.target)

[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0
 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1
 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1
 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0
 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1
 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1
 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1
 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0
 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1
 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1
 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1
 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0
 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 0 0 0 0 0 0 1]


# Import train_test_split function
from sklearn.model_selection import train_test_split
# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.2, random_state=0)


#Import svm model
from sklearn import svm
#Create a svm Classifier
clf = svm. SVC(kernel='linear') # Linear Kernel
#Train the model using the training sets
clf.fit(X_train, y_train)
#Predict the response for test dataset
y_pred = clf.predict(X_test)


#Import scikit-learn metrics module for accuracy calculation
from sklearn import metrics
score = metrics.accuracy_score(y_test, y_pred)
# Model Accuracy: how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

Accuracy: 0.956140350877193


# Model Precision: what percentage of positive tuples are labeled as such?
print("Precision:", metrics.precision_score(y_test, y_pred))
# Model Recall: what percentage of positive tuples are labelled as such?
print("Recall:", metrics.recall_score(y_test, y_pred))

Precision: 0.984375
Recall: 0.9402985074626866


# confusion matrix
from sklearn import metrics
cm = metrics.confusion_matrix(y_test, y_pred)
cm

array([[46,  1],
       [ 4, 63]], dtype=int64)


import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(12,12))
sns.heatmap(cm, annot=True, fmt=".3f", linewidths=.5, square = True, cmap = 'Spectral')
plt.ylabel('Actual label')
plt.xlabel('Predicted label')
all_sample_title = 'SVM model accuracy(in %): {0}'.format(score*100)
plt.title(all_sample_title, size = 15)

Text(0.5, 1.0, 'SVM model accuracy(in %): 95.6140350877193')


#importing libraries
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
from sklearn.cluster import KMeans
import sklearn.datasets._samples_generator


#The code below will build a 2D dataset with four blobs.
from sklearn.datasets._samples_generator import make_blobs
X, y_true = make_blobs(n_samples=400, centers=4, cluster_std=0.60, random_state=0)


#visualizing the dataset.
plt.scatter(X[:, 0], X[:, 1], s=10); # S is size of dots in data we are visualizing 
plt.show()


#create a K – means object while specifying the number of clusters, train the model, and estimate as
kmeans = KMeans(n_clusters=4)
kmeans.fit(X)
y_kmeans = kmeans.predict(X)


#plot and visualize the cluster’s centers as determined by the k-means Python estimator
plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=20, cmap='summer')
centers = kmeans.cluster_centers_
plt.scatter(centers[:, 0], centers[:, 1], c='blue', s=100, alpha=0.9);
plt.show()


# Importing libraries 
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
from sklearn.cluster import KMeans


#Load the digit dataset from sklearn and create an object out of it.
#Additionally, we can get the total number of rows and the total number of columns in this dataset

from sklearn.datasets import load_digits
digits = load_digits()
digits.data.shape

(1797, 64)


#We may cluster the data in the same way that we did in Example 1 above
kmeans = KMeans(n_clusters=10, random_state=0)
clusters = kmeans.fit_predict(digits.data)
# Checking how many clusters are created by K-means and how many feature does we have 
kmeans.cluster_centers_.shape
#indicates that K-means generated 10 clusters with 64 features

(10, 64)


# Checking the centers of cluster 
fig, ax = plt.subplots(2, 5, figsize=(8, 3))
centers = kmeans.cluster_centers_.reshape(10, 8, 8)
for axi, center in zip(ax.flat, centers):
   axi.set(xticks=[], yticks=[])
   axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)

#As a result, we will receive the picture below, which shows clusters centers learned by k-means.


# Checking the learned cluster labels with the actual labels
from scipy.stats import mode
labels = np.zeros_like(clusters)
for i in range(10):
   mask = (clusters == i)
   labels[mask] = mode(digits.target[mask])[0]

# Following that, we can check the accuracy as follows:
from sklearn.metrics import accuracy_score
accuracy_score(digits.target, labels)

0.7935447968836951


# Importing libraries 
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 
import sklearn


#import the dataset and slice the important features
dataset = pd.read_csv('Mall_Customers.csv')
dataset.head()


X = dataset.iloc[:, [3, 4]].values
X

array([[ 15,  39],
       [ 15,  81],
       [ 16,   6],
       [ 16,  77],
       [ 17,  40],
       [ 17,  76],
       [ 18,   6],
       [ 18,  94],
       [ 19,   3],
       [ 19,  72],
       [ 19,  14],
       [ 19,  99],
       [ 20,  15],
       [ 20,  77],
       [ 20,  13],
       [ 20,  79],
       [ 21,  35],
       [ 21,  66],
       [ 23,  29],
       [ 23,  98],
       [ 24,  35],
       [ 24,  73],
       [ 25,   5],
       [ 25,  73],
       [ 28,  14],
       [ 28,  82],
       [ 28,  32],
       [ 28,  61],
       [ 29,  31],
       [ 29,  87],
       [ 30,   4],
       [ 30,  73],
       [ 33,   4],
       [ 33,  92],
       [ 33,  14],
       [ 33,  81],
       [ 34,  17],
       [ 34,  73],
       [ 37,  26],
       [ 37,  75],
       [ 38,  35],
       [ 38,  92],
       [ 39,  36],
       [ 39,  61],
       [ 39,  28],
       [ 39,  65],
       [ 40,  55],
       [ 40,  47],
       [ 40,  42],
       [ 40,  42],
       [ 42,  52],
       [ 42,  60],
       [ 43,  54],
       [ 43,  60],
       [ 43,  45],
       [ 43,  41],
       [ 44,  50],
       [ 44,  46],
       [ 46,  51],
       [ 46,  46],
       [ 46,  56],
       [ 46,  55],
       [ 47,  52],
       [ 47,  59],
       [ 48,  51],
       [ 48,  59],
       [ 48,  50],
       [ 48,  48],
       [ 48,  59],
       [ 48,  47],
       [ 49,  55],
       [ 49,  42],
       [ 50,  49],
       [ 50,  56],
       [ 54,  47],
       [ 54,  54],
       [ 54,  53],
       [ 54,  48],
       [ 54,  52],
       [ 54,  42],
       [ 54,  51],
       [ 54,  55],
       [ 54,  41],
       [ 54,  44],
       [ 54,  57],
       [ 54,  46],
       [ 57,  58],
       [ 57,  55],
       [ 58,  60],
       [ 58,  46],
       [ 59,  55],
       [ 59,  41],
       [ 60,  49],
       [ 60,  40],
       [ 60,  42],
       [ 60,  52],
       [ 60,  47],
       [ 60,  50],
       [ 61,  42],
       [ 61,  49],
       [ 62,  41],
       [ 62,  48],
       [ 62,  59],
       [ 62,  55],
       [ 62,  56],
       [ 62,  42],
       [ 63,  50],
       [ 63,  46],
       [ 63,  43],
       [ 63,  48],
       [ 63,  52],
       [ 63,  54],
       [ 64,  42],
       [ 64,  46],
       [ 65,  48],
       [ 65,  50],
       [ 65,  43],
       [ 65,  59],
       [ 67,  43],
       [ 67,  57],
       [ 67,  56],
       [ 67,  40],
       [ 69,  58],
       [ 69,  91],
       [ 70,  29],
       [ 70,  77],
       [ 71,  35],
       [ 71,  95],
       [ 71,  11],
       [ 71,  75],
       [ 71,   9],
       [ 71,  75],
       [ 72,  34],
       [ 72,  71],
       [ 73,   5],
       [ 73,  88],
       [ 73,   7],
       [ 73,  73],
       [ 74,  10],
       [ 74,  72],
       [ 75,   5],
       [ 75,  93],
       [ 76,  40],
       [ 76,  87],
       [ 77,  12],
       [ 77,  97],
       [ 77,  36],
       [ 77,  74],
       [ 78,  22],
       [ 78,  90],
       [ 78,  17],
       [ 78,  88],
       [ 78,  20],
       [ 78,  76],
       [ 78,  16],
       [ 78,  89],
       [ 78,   1],
       [ 78,  78],
       [ 78,   1],
       [ 78,  73],
       [ 79,  35],
       [ 79,  83],
       [ 81,   5],
       [ 81,  93],
       [ 85,  26],
       [ 85,  75],
       [ 86,  20],
       [ 86,  95],
       [ 87,  27],
       [ 87,  63],
       [ 87,  13],
       [ 87,  75],
       [ 87,  10],
       [ 87,  92],
       [ 88,  13],
       [ 88,  86],
       [ 88,  15],
       [ 88,  69],
       [ 93,  14],
       [ 93,  90],
       [ 97,  32],
       [ 97,  86],
       [ 98,  15],
       [ 98,  88],
       [ 99,  39],
       [ 99,  97],
       [101,  24],
       [101,  68],
       [103,  17],
       [103,  85],
       [103,  23],
       [103,  69],
       [113,   8],
       [113,  91],
       [120,  16],
       [120,  79],
       [126,  28],
       [126,  74],
       [137,  18],
       [137,  83]], dtype=int64)


#find the optimal K value for clustering the data.
#Now we are using the Elbow method to find the optimal K value.
from sklearn.cluster import KMeans
wcss = [] 
for i in range(1, 11): 
    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
    #“init” argument is the method for initializing the centroid

    kmeans.fit(X) 
    wcss.append(kmeans.inertia_)
    #We calculated the WCSS value for each K value.


# Now we have to plot the WCSS with K value
plt.plot(range(1, 11), wcss)
plt.xlabel('Number of clusters')
plt.ylabel('WCSS') 
plt.show()
#The point at which the elbow shape is created is 5, that is, our K value or an optimal number of clusters is 5.


#Now let’s train the model on the dataset with a number of clusters 5.
kmeans = KMeans(n_clusters = 5, init = "k-means++", random_state = 42)
y_kmeans = kmeans.fit_predict(X)
y_kmeans
#y_kmeans give us different clusters corresponding to X.

array([2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
       2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 0,
       2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4, 0, 4, 1, 4, 1, 4,
       0, 4, 1, 4, 1, 4, 1, 4, 1, 4, 0, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,
       1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,
       1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,
       1, 4])


#Now let’s plot all the clusters using matplotlib.
plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 60, c = 'red', label = 'Cluster1')
plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 60, c = 'blue', label = 'Cluster2')
plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 60, c = 'green', label = 'Cluster3')
plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 60, c = 'violet', label = 'Cluster4')
plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 60, c = 'yellow', label = 'Cluster5') 
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'black', label = 'Centroids')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend() 

plt.show()

	total_bill	tip	size
count	244.000000	244.000000	244.000000
mean	19.785943	2.998279	2.569672
std	8.902412	1.383638	0.951100
min	3.070000	1.000000	1.000000
25%	13.347500	2.000000	2.000000
50%	17.795000	2.900000	2.000000
75%	24.127500	3.562500	3.000000
max	50.810000	10.000000	6.000000

	Gender	Location	Age_range	Qualification	field_of_study	Purpose_for_chilla	Work_status	Blood_group	Mobile_sim	Sim_type	...	Your favorite programming language?	Marital Status?	Are you Vaccinated?	Where do you live?	Working experience	Age	Weight_in_kg	Height_in_cm	How many hours you code a day? (int)	Light kitni der band hti hy? int
0	Male	Pakistan	36-40	Masters	Natural Sciences	to boost my skill set	Unemplyed	B+	U-fone	Prepaid	...	Python	Yes	Yes	Urbun	5.0	38.00	77.0	179.000	3.0	2
1	Male	Pakistan	26-30	Bachelors	CS/IT	to boost my skill set	Student	B+	U-fone	Prepaid	...	Python	No	Yes	Urbun	1.0	25.00	53.6	178.000	2.0	6
2	Male	Pakistan	31-35	Masters	Enginnering	Switch my field of study	Employed	B+	Zong	Prepaid	...	Python	Yes	Yes	Urbun	5.5	31.34	93.0	173.000	2.0	0
3	Female	Pakistan	31-35	Masters	CS/IT	to boost my skill set	Employed	O+	U-fone	Postpaid	...	Python	Yes	Yes	Urbun	5.0	33.00	60.0	157.000	3.0	24
4	Female	Pakistan	26-30	Masters	Enginnering	to boost my skill set	Student	A-	Mobilink	Prepaid	...	Javascript	No	Yes	Rural	3.5	27.00	59.9	164.544	6.0	12

	A	B	C	D	E
2022-01-01	-1.424660	0.772210	0.783466	-0.712659	-2.551065
2022-01-02	1.382429	-1.708730	1.176728	1.949761	-0.492011
2022-01-03	0.325283	-0.834825	-0.879866	-0.137038	0.919688
2022-01-04	0.552770	-0.454804	-0.747360	1.800993	-0.275458
2022-01-05	-0.896864	-0.958084	0.579021	0.478870	0.260666
2022-01-06	-3.103231	-0.340718	0.050150	0.058931	-1.712098
2022-01-07	1.769682	-1.402604	-2.110711	-0.922010	-0.753877
2022-01-08	0.660826	1.017961	0.569261	-1.209834	1.056544
2022-01-09	0.125091	-0.340940	1.023370	-1.555463	0.322947
2022-01-10	1.915190	0.314069	-0.638422	-1.023607	-1.544831
2022-01-11	-0.637742	-1.434976	-1.276443	-1.153942	0.761682
2022-01-12	-0.742913	-1.484980	-0.917490	-0.283180	-0.053965
2022-01-13	0.201580	0.786547	-1.459392	0.126138	-0.312369
2022-01-14	1.321449	0.050355	-0.933186	-1.043294	1.676111
2022-01-15	-0.436737	-0.262544	0.686336	0.144395	0.625462
2022-01-16	-0.152087	0.789873	-1.096439	-0.172554	-1.109436
2022-01-17	0.379340	-0.193847	-1.390857	-1.056517	-0.158073
2022-01-18	0.785079	-1.145713	0.233175	-0.294474	-0.493845
2022-01-19	-1.454019	0.974247	-0.442348	0.044247	1.190501
2022-01-20	0.503909	-0.776441	-0.323965	0.813752	-0.409261
2022-01-21	-0.270772	0.698006	-0.801287	-0.182554	0.852357
2022-01-22	-0.528683	0.258139	0.589976	0.325295	1.688333
2022-01-23	0.240170	-0.518060	0.402815	-1.852271	-0.960517
2022-01-24	0.615130	-0.661989	1.052420	0.529098	-2.626316
2022-01-25	-0.344456	1.276411	-0.625939	-1.117180	-0.680828
2022-01-26	-0.073498	0.611255	1.133547	0.358266	-0.694200
2022-01-27	-1.981745	0.243877	-0.122814	0.343524	1.155793
2022-01-28	-0.023202	0.546315	-0.150933	-0.316904	0.220736
2022-01-29	0.201237	0.241144	1.538946	-0.113433	2.243980
2022-01-30	0.073743	-0.597354	-0.399961	0.624458	-0.305179
2022-01-31	-0.752144	-0.738474	0.248588	0.375347	0.729071
2022-02-01	0.635539	0.457294	-0.037721	-0.169999	-0.373402
2022-02-02	1.761750	0.542268	0.059822	0.817176	0.607860

	A	B	C	D	E
2022-02-01	0.635539	0.457294	-0.037721	-0.169999	-0.373402
2022-02-02	1.761750	0.542268	0.059822	0.817176	0.607860

	A	B	C	D	E
count	20.000000	20.000000	20.000000	20.000000	20.000000
mean	-0.191240	-0.088290	0.036053	-0.406988	-0.216670
std	0.984138	0.733233	1.064758	1.084846	0.969833
min	-1.674983	-1.623248	-1.627397	-2.786986	-2.336489
25%	-0.883313	-0.613603	-0.790966	-0.899957	-0.582249
50%	-0.249756	-0.104036	0.078285	-0.285417	-0.196994
75%	0.532178	0.342963	0.850827	0.362692	0.271251
max	1.681379	1.307620	2.047838	1.267730	1.647141

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	virginica
146	6.3	2.5	5.0	1.9	virginica
147	6.5	3.0	5.2	2.0	virginica
148	6.2	3.4	5.4	2.3	virginica
149	5.9	3.0	5.1	1.8	virginica

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

	A	B	C	D	E	F
0	1.0	2013-01-02	1.0	3	test	foo
1	1.0	2013-01-02	1.0	3	train	foo
2	1.0	2013-01-02	1.0	3	test	foo
3	1.0	2013-01-02	1.0	3	train	foo

	A	B	C	D	E
2022-01-01	0.388135	-0.694500	1.710742	-0.953853	1.647141
2022-01-02	-0.517575	-0.188147	0.630036	-0.881991	-0.981272
2022-01-03	-1.674983	-0.586637	-1.340947	0.702661	-0.929540
2022-01-04	0.581906	-0.080027	1.137075	0.775445	-1.796040
2022-01-05	-1.273825	0.312914	-1.165314	-0.169640	-0.368941
2022-01-06	0.515602	0.490068	-0.545239	-0.408955	1.333627
2022-01-07	0.169663	-1.019295	-0.668360	-2.786986	-0.199359
2022-01-08	-0.130497	0.102798	0.288203	-0.291382	-0.364559
2022-01-09	-0.369015	1.283904	0.839903	-0.279453	-1.272016
2022-01-10	-0.814083	-0.705237	-1.627397	-2.385590	-0.144798
2022-01-11	-1.542745	-0.536425	0.484937	-0.486977	0.237764
2022-01-12	-0.882890	0.175792	-0.131634	0.699205	0.792908
2022-01-13	1.235072	0.640500	-1.283168	-1.422045	-0.402504
2022-01-14	-0.085713	0.117496	2.047838	0.240406	0.784371
2022-01-15	1.157494	-0.128045	-0.278789	0.296545	0.371711
2022-01-16	-0.884580	-1.623248	0.492578	0.088836	-0.049226
2022-01-17	-1.324278	-0.765477	0.907903	-0.808565	0.004943
2022-01-18	1.681379	-0.302969	-1.158782	1.267730	-2.336489
2022-01-19	0.821827	1.307620	-0.502119	-1.896293	-0.194628
2022-01-20	-0.875702	0.433110	0.883599	0.561136	-0.466486

	0	1	2	3
A	1.0	1.0	1.0	1.0
B	2013-01-02 00:00:00	2013-01-02 00:00:00	2013-01-02 00:00:00	2013-01-02 00:00:00
C	1.0	1.0	1.0	1.0
D	3	3	3	3
E	test	train	test	train
F	foo	foo	foo	foo

	A	B	C	D	E
2022-01-01	-1.424660	0.77221	0.783466	-0.712659	-2.551065
2022-01-02	1.382429	-1.70873	1.176728	1.949761	-0.492011

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
886	0	2	male	27.0	0	0	13.00	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.00	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.45	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.00	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.75	Q	Third	man	True	NaN	Queenstown	no	True

	survived	pclass	age	sibsp	parch	fare
count	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

	survived	pclass	age	sibsp	parch	fare
count	712.000000	712.000000	712.000000	712.000000	712.000000	712.000000
mean	0.404494	2.240169	29.642093	0.514045	0.432584	34.567251
std	0.491139	0.836854	14.492933	0.930692	0.854181	52.938648
min	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	0.000000	1.000000	20.000000	0.000000	0.000000	8.050000
50%	0.000000	2.000000	28.000000	0.000000	0.000000	15.645850
75%	1.000000	3.000000	38.000000	1.000000	1.000000	33.000000
max	1.000000	3.000000	80.000000	5.000000	6.000000	512.329200

	survived	pclass	age	sibsp	parch	fare	adult_male	alone
survived	1.000000	-0.356549	-0.074335	-0.014483	0.095426	0.273531	-0.554567	-0.201175
pclass	-0.356549	1.000000	-0.365121	0.061354	0.022519	-0.617591	0.102930	0.156030
age	-0.074335	-0.365121	1.000000	-0.308906	-0.186271	0.103100	0.275035	0.187284
sibsp	-0.014483	0.061354	-0.308906	1.000000	0.381803	0.197954	-0.311622	-0.629200
parch	0.095426	0.022519	-0.186271	0.381803	1.000000	0.259948	-0.366540	-0.574701
fare	0.273531	-0.617591	0.103100	0.197954	0.259948	1.000000	-0.228675	-0.333949
adult_male	-0.554567	0.102930	0.275035	-0.311622	-0.366540	-0.228675	1.000000	0.402214
alone	-0.201175	0.156030	0.187284	-0.629200	-0.574701	-0.333949	0.402214	1.000000

	survived	pclass	sex	age in days	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
1	1	1	female	13870.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
3	1	1	female	12775.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
6	0	1	male	19710.0	0	0	51.8625	S	First	man	True	E	Southampton	no	True
10	1	3	female	1460.0	1	1	16.7000	S	Third	child	False	G	Southampton	yes	False
11	1	1	female	21170.0	0	0	26.5500	S	First	woman	False	C	Southampton	yes	True

	survived	pclass	sex	age in days	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
1	1	1	female	13870	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
3	1	1	female	12775	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
6	0	1	male	19710	0	0	51.8625	S	First	man	True	E	Southampton	no	True
10	1	3	female	1460	1	1	16.7000	S	Third	child	False	G	Southampton	yes	False
11	1	1	female	21170	0	0	26.5500	S	First	woman	False	C	Southampton	yes	True

	age in days	fare
1	0.4750	0.139136
3	0.4375	0.103644
6	0.6750	0.101229
10	0.0500	0.032596
11	0.7250	0.051822

	age in days	fare
1	0.4750	-0.067057
3	0.4375	-0.309853
6	0.6750	-0.326377
10	0.0500	-0.795891
11	0.7250	-0.664367

	survived	pclass	sex	age in days	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
1	1	1	female	jawan	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
3	1	1	female	jawan	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
6	0	1	male	Boorhay	0	0	51.8625	S	First	man	True	E	Southampton	no	True
10	1	3	female	bachay	1	1	16.7000	S	Third	child	False	G	Southampton	yes	False
11	1	1	female	Boorhay	0	0	26.5500	S	First	woman	False	C	Southampton	yes	True

	age	sex	bmi	children	smoker	region	charges
0	19	female	27.900	0	yes	southwest	16884.92400
1	18	male	33.770	1	no	southeast	1725.55230
2	28	male	33.000	3	no	southeast	4449.46200
3	33	male	22.705	0	no	northwest	21984.47061
4	32	male	28.880	0	no	northwest	3866.85520

	age	distance	YearsExperience	Salary
0	31.1	77.75	1.1	39343
1	31.3	78.25	1.3	46205
2	31.5	78.75	1.5	37731
3	32.0	80.00	2.0	43525
4	32.2	80.50	2.2	39891

	year	month	day	week	temp_2	temp_1	average	actual	forecast_noaa	forecast_acc	forecast_under	friend
0	2016	1	1	Fri	45	45	45.6	45	43	50	44	29
1	2016	1	2	Sat	44	45	45.7	44	41	50	44	61
2	2016	1	3	Sun	45	44	45.8	41	43	46	47	56
3	2016	1	4	Mon	44	41	45.9	40	44	48	46	53
4	2016	1	5	Tues	41	40	46.0	44	46	46	46	41

	Position	Level	Salary
0	Business Analyst	1	45000
1	Junior Consultant	2	50000
2	Senior Consultant	3	60000
3	Manager	4	80000
4	Country Manager	5	110000

Pyhton ka chilla¶

Chapter - 1 How to use jupyter notebook¶

Our basics of python¶

01-Our First Program¶

02-Operators¶

03-Strings¶

04-Comments¶

05-Variable¶

06-Input Variables¶

07-Conditional Logics¶

08-Type Conversion¶

09-if, else and elif¶

10-Functions¶

11-Loops¶

12-Import Library¶

13-TroubleShooting¶

14-Practice¶

BMI Calculator¶

Chapter 2- Basics¶

Indexing¶

String Methods¶

Basic data Structure in Python¶

1-Tuple¶

2-List¶

3-Dictionaries¶

4-Set¶

Tuple¶

-indexing in tuple¶

List¶

3- Dictionaries¶

4-Sets¶

Chapter 3¶

Graphs¶

First graph¶

Second Graph¶

01_Line Plot¶

Impot library¶

Adding limits¶

Set style¶

Size of figure¶

Line Plot assignment 1¶

02_Bar Plot¶

BoxPlot_ Assignment 2¶

Assignment with new data set¶

Chapter 4- Numpy¶

1-D Array¶

2-D Array¶

2- axis¶

3-D Array¶

Numpy Practice Session¶

creating an array using numpy¶

Array functions¶

# 3-D Array¶

converting 1d to 2d¶

Chapter 5- Pandas¶

How to install library¶

Importing Libraries¶

Assignment¶

Assignment¶

Chapter 6- Exploratory Data Analysis¶

Three important steps to keep in mind are :¶

cleaning and filtering the data¶

Chapter 7 - Data Wrangling¶

Dealing with missing values¶

Replacing missing values with the average of that column¶

Assignment¶

Data Formatting¶

Data Normailization¶

Method of Normalization¶

Binning¶

Converting catagories into dummies¶

Chapter 8- Statistics¶

Types of Experimental Designs | Statistics¶

1. Completely Randomized Design (CRD):¶

2. Randomized Block Design (RBD):¶

3. Latin Square Design (LSD):¶

4. Split Plot Design (SPD):¶

5. Lattice Design:¶

6. Augmented Designs:¶

Tests and their types¶

	age	height	weight	gender	likeness
0	27	170.688	76.0	Male	Biryani
1	41	165.000	70.0	Male	Biryani
2	29	171.000	80.0	Male	Biryani
3	27	173.000	102.0	Male	Biryani
4	29	164.000	67.0	Male	Biryani

	age	height	weight	gender	likeness
240	31	160.0	60.0	1	Pakora
241	26	172.0	70.0	1	Biryani
242	40	178.0	80.0	1	Biryani
243	25	5.7	65.0	1	Biryani
244	33	157.0	56.0	0	Samosa

	sepal_length	sepal_width	petal_length	petal_width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2

Clustering	Classification
Un Supervised	Supervised
No training data	Labeled data
Unalbeled data	Labeled data
Define labels using data then in algorithm	Data>Model>Training>Classification